Piris Labs growth playbook

Serve real HTML for all 26 sitemap URLs, using prerendering or static export from the current framework so the design stays the same. Give each page its own title, a 150-character description and an Open Graph image. Put the benchmark numbers in the HTML of /news/glm-benchmark. Add Organization schema to the homepage, Article schema to news posts and JobPosting schema to careers posts. Then resubmit the sitemap to Google Search Console and Bing Webmaster Tools. The payoff: when an inference buyer or an AI assistant searches "fastest GLM-5.2 inference", Piris Labs can be the cited source, and a shared benchmark link shows a headline and number instead of a bare title.

Search engines, AI assistants and social previews can only rank, cite or show what is in the HTML. For an eight-person company with no ad budget, organic discovery and shared links are the main channels, and right now both carry nothing.

[ ] Benchmark numbers visible in page source with JavaScript off
[ ] Unique <title> on all 26 sitemap URLs (no repeats of the homepage title)
[ ] Meta description on every page
[ ] og:title, og:description, og:image on every page
[ ] Organization, Article and JobPosting schema
[ ] Canonical tag per page
[ ] Sitemap resubmitted to Google and Bing
[ ] LinkedIn and X previews checked for the benchmark link

• Prerendering breaks interactive parts of the site

  Prerender content routes only and keep forms client-side.

• Engineering time pulled from the inference stack

  Start with the benchmark page alone; it is the page both launches link to.

• Your websiteRendered HTML (fetched twice without JavaScript, November 9, 2026): the homepage, /news/glm-benchmark, /careers, /gpu-compute and /early-access each return only the title "Piris Labs | Optical Systems for AI Infrastructure", with no body text, meta description or Open Graph tags; /products returns only "Products | Piris Labs". robots.txt allows all crawlers, yet a site:pirislabs.io web search returned no pages from the site.Source
• Your websiteSitemap (26 URLs, checked November 9, 2026): four product pages (comb-light-engine, 1-6tbps-optical-transceiver, optical-pcie, ai-endpoint); seven news posts (glm-benchmark, rf-characterization-lab, nvidia-inception, yc-funding, optical-loss, datacenter-architecture, marc-baldo-advisory); four careers posts (systems-engineer-inference, optical-scientist, extreme-rf-systems-engineer, process-innovation-engineer); plus /early-access, /gpu-compute, /team and /contact. No pricing, documentation, benchmark-method or use-case pages.Source
• LaunchYC launch "We Set the Fastest Reported GLM-5.2 Inference Speed": "outperforming pioneers and veterans in the space"; the launch itself gives no tokens-per-second or latency figures and points to pirislabs.io/news/glm-benchmark; "We welcome independent third-party benchmarking."Source

Contact Artificial Analysis about being benchmarked for GLM-5.2, providing a stable endpoint and the model variant and precision used. Meanwhile, publish a reproducible test script so any buyer can measure tokens per second and time to first token themselves. Link the independent result from the benchmark page and both launch posts.

A third-party number is worth more than a self-reported record, and it puts Piris Labs in the table where inference buyers already shortlist providers.

• The independent result is slower than the internal record

  Self-test first using the public method and publish both numbers with the conditions behind each.

• Capacity limits during testing

  Reserve a dedicated endpoint for the benchmark window.

• #3 Publish the benchmark's numbers and method on the page itself: model variant, precision, tokens per second, time to first token, date

• Third-party benchmarkArtificial Analysis GLM-5.2 provider page (checked November 9, 2026): 17 providers benchmarked, including Baseten, CoreWeave, Databricks, DeepInfra, Fireworks, FriendliAI, Mistral, Nebius, Together AI and Zai; Piris Labs is not listed. Shown prices include CoreWeave at $0.76 input and $2.42 output, and DeepInfra (FP4) at $0.56 and $1.80, per million tokens.See the research Source
• LaunchYC launch "We Set the Fastest Reported GLM-5.2 Inference Speed": "outperforming pioneers and veterans in the space"; the launch itself gives no tokens-per-second or latency figures and points to pirislabs.io/news/glm-benchmark; "We welcome independent third-party benchmarking."Source

Lead the benchmark page with one table: model and variant, precision, input and output lengths, output tokens per second, time to first token, concurrency, hardware, date and how it was measured. Add a chart against named public figures with their source and date, and a downloadable raw log. Reuse the table in the launch posts and sales emails.

Technical buyers trust a method they can reproduce. A specific table also gives AI assistants and journalists a precise number to quote.

• Comparisons go stale as rivals improve

  Date every number and refresh monthly.

• #1 Make the GLM-5.2 benchmark readable: every pirislabs.io page serves search engines, AI assistants and link previews nothing but a title

• LaunchYC launch "We Set the Fastest Reported GLM-5.2 Inference Speed": "outperforming pioneers and veterans in the space"; the launch itself gives no tokens-per-second or latency figures and points to pirislabs.io/news/glm-benchmark; "We welcome independent third-party benchmarking."Source
• Your websiteRendered HTML (fetched twice without JavaScript, November 9, 2026): the homepage, /news/glm-benchmark, /careers, /gpu-compute and /early-access each return only the title "Piris Labs | Optical Systems for AI Infrastructure", with no body text, meta description or Open Graph tags; /products returns only "Products | Piris Labs". robots.txt allows all crawlers, yet a site:pirislabs.io web search returned no pages from the site.Source
• Third-party benchmarkArtificial Analysis GLM-5.2 provider page (checked November 9, 2026): 17 providers benchmarked, including Baseten, CoreWeave, Databricks, DeepInfra, Fireworks, FriendliAI, Mistral, Nebius, Together AI and Zai; Piris Labs is not listed. Shown prices include CoreWeave at $0.76 input and $2.42 output, and DeepInfra (FP4) at $0.56 and $1.80, per million tokens.See the research Source

Turn /early-access into a two-way choice. The inference path asks for model, monthly tokens, latency target and use case (voice, coding agent, other). The hardware path asks for product type, interconnect speed, volume and timeline. Route each to the right founder with its own follow-up email and booking link, and tag every request so the pipeline can be counted by buyer type.

Qualifying questions that match how each buyer thinks shorten the first call and show which side of the business is pulling demand.

• Longer forms reduce requests

  Keep five questions and make the rest optional.

• LaunchYC launch "Inference at Light Speed": proprietary optical interconnects plus a purpose-built software stack to maximise hardware utilisation; claims 5x lower latency, 10x lower power per bit and 2x lower cost per token; two asks: AI product companies looking to scale inference workloads, and chip makers and ODM partners to try the optical solution first-hand.Source
• Your websiteSitemap (26 URLs, checked November 9, 2026): four product pages (comb-light-engine, 1-6tbps-optical-transceiver, optical-pcie, ai-endpoint); seven news posts (glm-benchmark, rf-characterization-lab, nvidia-inception, yc-funding, optical-loss, datacenter-architecture, marc-baldo-advisory); four careers posts (systems-engineer-inference, optical-scientist, extreme-rf-systems-engineer, process-innovation-engineer); plus /early-access, /gpu-compute, /team and /contact. No pricing, documentation, benchmark-method or use-case pages.Source
• Directory listingsRespan market map: Piris Labs under Inference & Compute with a single plan, Early Access, priced on request; 30 alternatives listed, with Cerebras, Groq and SambaNova named as the closest hardware rivals; the listing notes the performance claims are unverified.Source

Publish an indicative GLM-5.2 price per million input and output tokens for early-access customers, with a volume tier and a "talk to us above X tokens a month" line. Show it beside the speed, compared with named public prices and their dates. Keep custom deployments on request.

A visible price turns "2x cheaper" from a claim into a number a developer can put in a spreadsheet, and filters out leads who cannot afford the service.

• Price too low for limited capacity

  Label it as early-access pricing with a cap on monthly tokens.

• Directory listingsRespan market map: Piris Labs under Inference & Compute with a single plan, Early Access, priced on request; 30 alternatives listed, with Cerebras, Groq and SambaNova named as the closest hardware rivals; the listing notes the performance claims are unverified.Source
• Third-party benchmarkArtificial Analysis GLM-5.2 provider page (checked November 9, 2026): 17 providers benchmarked, including Baseten, CoreWeave, Databricks, DeepInfra, Fireworks, FriendliAI, Mistral, Nebius, Together AI and Zai; Piris Labs is not listed. Shown prices include CoreWeave at $0.76 input and $2.42 output, and DeepInfra (FP4) at $0.56 and $1.80, per million tokens.See the research Source
• LaunchYC launch "Inference at Light Speed": proprietary optical interconnects plus a purpose-built software stack to maximise hardware utilisation; claims 5x lower latency, 10x lower power per bit and 2x lower cost per token; two asks: AI product companies looking to scale inference workloads, and chip makers and ODM partners to try the optical solution first-hand.Source

List all five roles on YC's Work at a Startup board and LinkedIn, with the same salary and equity ranges the founding engineer post shows. Add JobPosting structured data to each careers page once it is readable (idea 1). Link the RF lab and optical loss posts from the hardware roles, so candidates see the work.

Specialist optical and RF candidates are rare and search the main boards. Roles they cannot find do not get filled.

• Too many unqualified applicants

  Add one screening question per role.

• #1 Make the GLM-5.2 benchmark readable: every pirislabs.io page serves search engines, AI assistants and link previews nothing but a title

• Your websiteSitemap (26 URLs, checked November 9, 2026): four product pages (comb-light-engine, 1-6tbps-optical-transceiver, optical-pcie, ai-endpoint); seven news posts (glm-benchmark, rf-characterization-lab, nvidia-inception, yc-funding, optical-loss, datacenter-architecture, marc-baldo-advisory); four careers posts (systems-engineer-inference, optical-scientist, extreme-rf-systems-engineer, process-innovation-engineer); plus /early-access, /gpu-compute, /team and /contact. No pricing, documentation, benchmark-method or use-case pages.Source
• YC profileTagline "The Networking Layer of AI"; says up to 80% of advanced AI compute chips sit idle because of slow networking; founded 2025, team of 8, San Francisco, Winter 2026 batch; the President leads the inference service and customer onboarding; one open job listed on YC.Source
• Your websiteRendered HTML (fetched twice without JavaScript, November 9, 2026): the homepage, /news/glm-benchmark, /careers, /gpu-compute and /early-access each return only the title "Piris Labs | Optical Systems for AI Infrastructure", with no body text, meta description or Open Graph tags; /products returns only "Products | Piris Labs". robots.txt allows all crawlers, yet a site:pirislabs.io web search returned no pages from the site.Source

Build /voice: a page showing time to first token and end-to-end turn latency for a voice-sized prompt on Piris Labs, compared with named public figures. Add a one-click latency test against the endpoint, a short integration guide for common voice stacks, and a voice-specific early-access path. Run outreach to voice-AI startups with their own measured latency.

Latency is the buying criterion for voice teams, and a narrow page speaks to it more clearly than "fastest inference".

• Network distance hides server gains

  Report server-side and end-to-end figures separately.

• #1 Make the GLM-5.2 benchmark readable: every pirislabs.io page serves search engines, AI assistants and link previews nothing but a title

• HiringFounding Engineer, AI Inference Stack (San Francisco, in person, $100K-$200K, 0.10%-1.00% equity, new grads OK): orchestrate inference across heterogeneous compute clusters; write kernels in CUDA or Triton; build a low-latency inference server in the vein of vLLM or TensorRT-LLM, with KV cache management that avoids PCIe bottlenecks; named focus areas are instant-on voice AI and memory management for coding agents.Source
• Third-party benchmarkArtificial Analysis GLM-5.2 provider page (checked November 9, 2026): 17 providers benchmarked, including Baseten, CoreWeave, Databricks, DeepInfra, Fireworks, FriendliAI, Mistral, Nebius, Together AI and Zai; Piris Labs is not listed. Shown prices include CoreWeave at $0.76 input and $2.42 output, and DeepInfra (FP4) at $0.56 and $1.80, per million tokens.See the research Source
• LaunchYC launch "Inference at Light Speed": proprietary optical interconnects plus a purpose-built software stack to maximise hardware utilisation; claims 5x lower latency, 10x lower power per bit and 2x lower cost per token; two asks: AI product companies looking to scale inference workloads, and chip makers and ODM partners to try the optical solution first-hand.Source

Build /coding-agents with measured speed and cost at long context lengths and repeated prefixes, compared with a standard GPU deployment. Explain the memory-wall advantage in one diagram. Offer agent teams a cost-per-task estimate from their own logs.

Agent builders feel long-context cost directly in their bills, so a memory-focused proof point speaks their language better than raw tokens per second.

• Customers will not share logs

  Accept summary statistics instead of raw logs.

• #1 Make the GLM-5.2 benchmark readable: every pirislabs.io page serves search engines, AI assistants and link previews nothing but a title

• HiringFounding Engineer, AI Inference Stack (San Francisco, in person, $100K-$200K, 0.10%-1.00% equity, new grads OK): orchestrate inference across heterogeneous compute clusters; write kernels in CUDA or Triton; build a low-latency inference server in the vein of vLLM or TensorRT-LLM, with KV cache management that avoids PCIe bottlenecks; named focus areas are instant-on voice AI and memory management for coding agents.Source
• PressTekedia Capital investment note (March 18, 2026): a platform pairing photonic hardware with its own software stack to break the "memory wall", claiming performance comparable to traditional compute clusters at lower cost; advisors include the Director of MIT's Research Laboratory of Electronics and a former Groq executive.Source

Offer a capped self-serve key for GLM-5.2 on an OpenAI-compatible endpoint, with a quickstart that runs in one copy-paste and a small free token allowance. Gate higher limits behind the early-access conversation. Publish documentation at /docs.

Developers judge speed by trying it. A self-serve key turns the benchmark claim into a first-hand experience and brings in leads without a sales call.

• Trial traffic eats scarce capacity

  Cap free tokens and queue trial traffic behind paid traffic.

• #5 Put a price per million tokens next to the speed, against the providers buyers already see

• Competitor researchRivals' homepages: Cerebras ("The World's Fastest AI") offers a free trial on its Inference Cloud, OpenAI API compatibility and setup "in <30 seconds"; Groq calls itself "the premier neocloud for fast inference" with a "Start building" developer console.See the research Source
• Your websiteSitemap (26 URLs, checked November 9, 2026): four product pages (comb-light-engine, 1-6tbps-optical-transceiver, optical-pcie, ai-endpoint); seven news posts (glm-benchmark, rf-characterization-lab, nvidia-inception, yc-funding, optical-loss, datacenter-architecture, marc-baldo-advisory); four careers posts (systems-engineer-inference, optical-scientist, extreme-rf-systems-engineer, process-innovation-engineer); plus /early-access, /gpu-compute, /team and /contact. No pricing, documentation, benchmark-method or use-case pages.Source
• Directory listingsRespan market map: Piris Labs under Inference & Compute with a single plan, Early Access, priced on request; 30 alternatives listed, with Cerebras, Groq and SambaNova named as the closest hardware rivals; the listing notes the performance claims are unverified.Source

Add a spec card to each hardware page: bandwidth, energy per bit (pJ/bit), reach, form factor, interface, wavelength count and availability (sample, evaluation, volume). Offer a one-page PDF datasheet per product behind a short form. Mark anything still in development clearly.

Hardware engineers shortlist by spec table. Absolute numbers make the 10x claim comparable and give ODM partners something to forward internally.

• Disclosing too much to competitors

  Publish headline specs and keep detailed data under NDA.

• #1 Make the GLM-5.2 benchmark readable: every pirislabs.io page serves search engines, AI assistants and link previews nothing but a title

• Competitor researchLightmatter ("Photonic interconnects that scale AI supercomputers") publishes per-product specs, such as Passage L200 at 32-64 Tbps and Passage EVK50 at 800 Gbps per fiber and 2.6 pJ/bit, sells evaluation kits (Passage M1000 EVK, EVK100, EVK50, Guide 1 EVK) and has joined the NVIDIA NVLink Fusion ecosystem.See the research Source
• Your websiteSitemap (26 URLs, checked November 9, 2026): four product pages (comb-light-engine, 1-6tbps-optical-transceiver, optical-pcie, ai-endpoint); seven news posts (glm-benchmark, rf-characterization-lab, nvidia-inception, yc-funding, optical-loss, datacenter-architecture, marc-baldo-advisory); four careers posts (systems-engineer-inference, optical-scientist, extreme-rf-systems-engineer, process-innovation-engineer); plus /early-access, /gpu-compute, /team and /contact. No pricing, documentation, benchmark-method or use-case pages.Source
• LaunchYC launch "Inference at Light Speed": proprietary optical interconnects plus a purpose-built software stack to maximise hardware utilisation; claims 5x lower latency, 10x lower power per bit and 2x lower cost per token; two asks: AI product companies looking to scale inference workloads, and chip makers and ODM partners to try the optical solution first-hand.Source

Package one evaluation kit, such as the 1.6 Tbps transceiver on a test board with software and a lab guide, with a fixed fee, a 60-day evaluation and a joint test plan. Cap the programme at five partners, publish the application criteria and convert finished evaluations into design-in agreements.

A defined kit and fee filter serious partners, shorten the sales cycle and fund early hardware runs.

• Support load on a small team

  Cap at five partners.

• IP exposure

  NDA and evaluation agreement before shipping.

• #4 Split early access into two doors: an inference API for AI product teams and an evaluation programme for chip makers and ODMs
• #10 Spec cards for the transceiver, comb light engine and optical PCIe pages, in the units hardware buyers compare

• LaunchYC launch "Inference at Light Speed": proprietary optical interconnects plus a purpose-built software stack to maximise hardware utilisation; claims 5x lower latency, 10x lower power per bit and 2x lower cost per token; two asks: AI product companies looking to scale inference workloads, and chip makers and ODM partners to try the optical solution first-hand.Source
• Competitor researchLightmatter ("Photonic interconnects that scale AI supercomputers") publishes per-product specs, such as Passage L200 at 32-64 Tbps and Passage EVK50 at 800 Gbps per fiber and 2.6 pJ/bit, sells evaluation kits (Passage M1000 EVK, EVK100, EVK50, Guide 1 EVK) and has joined the NVIDIA NVLink Fusion ecosystem.See the research Source

Once a month, publish one engineering post on the site (readable, per idea 1) and cut it into a LinkedIn article, an X thread with one chart, and a short lab video. Rotate between optics, RF and inference software. End each piece with the relevant early-access path.

Deep-tech buyers and candidates follow engineering detail, and repeat posts build the credibility a young hardware company needs.

• Disclosing sensitive lab detail

  Founder review before each post.

• #1 Make the GLM-5.2 benchmark readable: every pirislabs.io page serves search engines, AI assistants and link previews nothing but a title

• Your websiteSitemap (26 URLs, checked November 9, 2026): four product pages (comb-light-engine, 1-6tbps-optical-transceiver, optical-pcie, ai-endpoint); seven news posts (glm-benchmark, rf-characterization-lab, nvidia-inception, yc-funding, optical-loss, datacenter-architecture, marc-baldo-advisory); four careers posts (systems-engineer-inference, optical-scientist, extreme-rf-systems-engineer, process-innovation-engineer); plus /early-access, /gpu-compute, /team and /contact. No pricing, documentation, benchmark-method or use-case pages.Source
• PressFondo launch recap (February 23, 2026): "The world's fastest, most cost-effective inference, driven by Piris Labs' breakthrough optical interconnects"; the launch cites the reported NVIDIA-Groq deal as precedent and asks readers to follow Piris Labs on LinkedIn or X.Source

Offer W26 and S26 AI companies a block of free GLM-5.2 tokens for 60 days in return for running real traffic, sharing latency and cost data, and agreeing to a short public case study if the numbers hold. Ask each to refer one other team.

Batch-mates trust each other, move fast and talk to each other, which makes them ideal first customers and referrers.

• Credits used but no conversion

  Require production traffic, not tests, and set a paid rate for after the credit period.

• YC profileTagline "The Networking Layer of AI"; says up to 80% of advanced AI compute chips sit idle because of slow networking; founded 2025, team of 8, San Francisco, Winter 2026 batch; the President leads the inference service and customer onboarding; one open job listed on YC.Source
• HiringFounding Engineer, AI Inference Stack (San Francisco, in person, $100K-$200K, 0.10%-1.00% equity, new grads OK): orchestrate inference across heterogeneous compute clusters; write kernels in CUDA or Triton; build a low-latency inference server in the vein of vLLM or TensorRT-LLM, with KV cache management that avoids PCIe bottlenecks; named focus areas are instant-on voice AI and memory management for coding agents.Source
• Directory listingsExtruct profile: classifies Piris Labs as "Software only" under "Builds Hardware" and as DevTools / low-latency inference, with no customers, pricing or funding figures shown.Source

Publish /status-speed with rolling 24-hour and 7-day median and p95 tokens per second and time to first token per model, measured from production traffic or a synthetic probe. Add uptime. Link it from the benchmark page and pricing.

A live number is harder to dismiss than a launch claim, and it shows reliability, which buyers ask about after speed.

• Bad days are public

  Show p95 and a short incident note; honesty builds trust.

• #3 Publish the benchmark's numbers and method on the page itself: model variant, precision, tokens per second, time to first token, date

• Directory listingsRespan market map: Piris Labs under Inference & Compute with a single plan, Early Access, priced on request; 30 alternatives listed, with Cerebras, Groq and SambaNova named as the closest hardware rivals; the listing notes the performance claims are unverified.Source
• LaunchYC launch "We Set the Fastest Reported GLM-5.2 Inference Speed": "outperforming pioneers and veterans in the space"; the launch itself gives no tokens-per-second or latency figures and points to pirislabs.io/news/glm-benchmark; "We welcome independent third-party benchmarking."Source

Claim or request edits on the main directory and data profiles (YC, Extruct, Respan, Crunchbase, LinkedIn): hardware plus inference, the four products, the GLM-5.2 result, pricing status and hiring. Use the same 50-word description everywhere, and make it match the homepage.

Consistent, correct descriptions across sources are what AI assistants and analysts repeat. One wrong field, such as "software only", can misroute every hardware partner who checks.

• Some directories ignore edit requests

  Prioritise the ones that rank for the company name.

• Directory listingsExtruct profile: classifies Piris Labs as "Software only" under "Builds Hardware" and as DevTools / low-latency inference, with no customers, pricing or funding figures shown.Source
• Directory listingsRespan market map: Piris Labs under Inference & Compute with a single plan, Early Access, priced on request; 30 alternatives listed, with Cerebras, Groq and SambaNova named as the closest hardware rivals; the listing notes the performance claims are unverified.Source
• Your websiteRendered HTML (fetched twice without JavaScript, November 9, 2026): the homepage, /news/glm-benchmark, /careers, /gpu-compute and /early-access each return only the title "Piris Labs | Optical Systems for AI Infrastructure", with no body text, meta description or Open Graph tags; /products returns only "Products | Piris Labs". robots.txt allows all crawlers, yet a site:pirislabs.io web search returned no pages from the site.Source

Position GPU compute as "start on GPUs, move to optics as capacity opens": the same API, the same contract, and a guaranteed migration price when optical capacity is available. Each GPU customer becomes a warm lead for the optical tier and a source of real workload data.

Customers commit to a known product now, and the migration promise turns the optical roadmap into a reason to sign rather than wait.

• Becoming another GPU reseller

  Cap GPU revenue share and tie every deal to a migration clause.

• #5 Put a price per million tokens next to the speed, against the providers buyers already see

• Your websiteSitemap (26 URLs, checked November 9, 2026): four product pages (comb-light-engine, 1-6tbps-optical-transceiver, optical-pcie, ai-endpoint); seven news posts (glm-benchmark, rf-characterization-lab, nvidia-inception, yc-funding, optical-loss, datacenter-architecture, marc-baldo-advisory); four careers posts (systems-engineer-inference, optical-scientist, extreme-rf-systems-engineer, process-innovation-engineer); plus /early-access, /gpu-compute, /team and /contact. No pricing, documentation, benchmark-method or use-case pages.Source
• HiringFounding Engineer, AI Inference Stack (San Francisco, in person, $100K-$200K, 0.10%-1.00% equity, new grads OK): orchestrate inference across heterogeneous compute clusters; write kernels in CUDA or Triton; build a low-latency inference server in the vein of vLLM or TensorRT-LLM, with KV cache management that avoids PCIe bottlenecks; named focus areas are instant-on voice AI and memory management for coding agents.Source

Rewrite /team as proof: for each founder and advisor, one line of shipped work (NASA mission hardware, a research record, a production system at scale) with links to papers and press. Add a "from lab to product" timeline. Use the same proof lines in hardware outreach and datasheets.

Chip makers and ODMs judge a young optics vendor by whether the team has shipped hard hardware before. Flight hardware for NASA answers that quickly.

• Reads as boastful

  State facts and link sources; skip adjectives.

• #1 Make the GLM-5.2 benchmark readable: every pirislabs.io page serves search engines, AI assistants and link previews nothing but a title

• PressMIT RLE founder profile (April 1, 2026): the CEO's earlier work includes the first portable terahertz laser systems, the local oscillator for NASA's GUSTO mission and ultra-low-loss photonic solutions for electro-optical interconnects; "Probably 90% of academic solutions remain in academia".Source
• PressTekedia Capital investment note (March 18, 2026): a platform pairing photonic hardware with its own software stack to break the "memory wall", claiming performance comparable to traditional compute clusters at lower cost; advisors include the Director of MIT's Research Laboratory of Electronics and a former Groq executive.Source
• Your websiteRendered HTML (fetched twice without JavaScript, November 9, 2026): the homepage, /news/glm-benchmark, /careers, /gpu-compute and /early-access each return only the title "Piris Labs | Optical Systems for AI Infrastructure", with no body text, meta description or Open Graph tags; /products returns only "Products | Piris Labs". robots.txt allows all crawlers, yet a site:pirislabs.io web search returned no pages from the site.Source

Instrument a production inference node to measure wall-plug energy per million tokens for GLM-5.2, split between compute and data movement. Publish the method and result next to a matched GPU baseline, refresh it quarterly, and offer customers an energy line on their invoice.

It turns a component claim into a buyer metric, gives the company a number competitors cannot quickly match, and appeals to data-centre partners limited by power.

• The system-level gain is smaller than the component claim

  Publish both and explain where the gain comes from.

• #14 A live speed page: rolling tokens per second and time to first token from production traffic

• LaunchYC launch "Inference at Light Speed": proprietary optical interconnects plus a purpose-built software stack to maximise hardware utilisation; claims 5x lower latency, 10x lower power per bit and 2x lower cost per token; two asks: AI product companies looking to scale inference workloads, and chip makers and ODM partners to try the optical solution first-hand.Source
• Competitor researchLightmatter ("Photonic interconnects that scale AI supercomputers") publishes per-product specs, such as Passage L200 at 32-64 Tbps and Passage EVK50 at 800 Gbps per fiber and 2.6 pJ/bit, sells evaluation kits (Passage M1000 EVK, EVK100, EVK50, Guide 1 EVK) and has joined the NVIDIA NVLink Fusion ecosystem.See the research Source
• Third-party benchmarkArtificial Analysis GLM-5.2 provider page (checked November 9, 2026): 17 providers benchmarked, including Baseten, CoreWeave, Databricks, DeepInfra, Fireworks, FriendliAI, Mistral, Nebius, Together AI and Zai; Piris Labs is not listed. Shown prices include CoreWeave at $0.76 input and $2.42 output, and DeepInfra (FP4) at $0.56 and $1.80, per million tokens.See the research Source

Commission an independent university or test-lab measurement of the optical link (bandwidth, pJ/bit, latency) and a system-level inference test against a matched GPU setup. Publish a co-authored whitepaper with full methods and data, then use it in partner outreach, fundraising and press.

Independent validation is what moves a deep-tech claim from "interesting" to "shortlisted" for chip makers, ODMs and large inference buyers.

• Results fall short of the headline

  Update the claims to the validated numbers; credibility is worth more than a bigger number.

• #10 Spec cards for the transceiver, comb light engine and optical PCIe pages, in the units hardware buyers compare

• Directory listingsRespan market map: Piris Labs under Inference & Compute with a single plan, Early Access, priced on request; 30 alternatives listed, with Cerebras, Groq and SambaNova named as the closest hardware rivals; the listing notes the performance claims are unverified.Source
• PressMIT RLE founder profile (April 1, 2026): the CEO's earlier work includes the first portable terahertz laser systems, the local oscillator for NASA's GUSTO mission and ultra-low-loss photonic solutions for electro-optical interconnects; "Probably 90% of academic solutions remain in academia".Source
• LaunchYC launch "Inference at Light Speed": proprietary optical interconnects plus a purpose-built software stack to maximise hardware utilisation; claims 5x lower latency, 10x lower power per bit and 2x lower cost per token; two asks: AI product companies looking to scale inference workloads, and chip makers and ODM partners to try the optical solution first-hand.Source

Offer one or two high-volume customers a dedicated optical pod: reserved capacity, a fixed price per million tokens, an agreed latency target and an option to expand. Use the commitment to finance the hardware build, and publish a case study with the customer's permission.

Anchor contracts de-risk hardware spending, prove the system at scale and give later buyers a reference.

• Customer concentration

  Cap any one customer's share of capacity.

• Delivery delays

  Run the customer on GPU capacity until the pod is live (idea 16).

• #16 Sell GPU compute now as the on-ramp to the optical tier

• PressTekedia Capital investment note (March 18, 2026): a platform pairing photonic hardware with its own software stack to break the "memory wall", claiming performance comparable to traditional compute clusters at lower cost; advisors include the Director of MIT's Research Laboratory of Electronics and a former Groq executive.Source
• Competitor researchRivals' homepages: Cerebras ("The World's Fastest AI") offers a free trial on its Inference Cloud, OpenAI API compatibility and setup "in <30 seconds"; Groq calls itself "the premier neocloud for fast inference" with a "Start building" developer console.See the research Source
• Directory listingsRespan market map: Piris Labs under Inference & Compute with a single plan, Early Access, priced on request; 30 alternatives listed, with Cerebras, Groq and SambaNova named as the closest hardware rivals; the listing notes the performance claims are unverified.Source

Measure accelerator utilisation across the company's own GPU and optical workloads, invite design partners to contribute anonymised traces, and publish an open dataset with an interactive explainer: where time goes (compute, memory, network) by model size and context length. Update it twice a year.

Original data earns links, citations and analyst attention, and every chart makes the case for fixing data movement.

• Measured idle time is below 80%

  Publish the real figure and update the profile claim to match.

• YC profileTagline "The Networking Layer of AI"; says up to 80% of advanced AI compute chips sit idle because of slow networking; founded 2025, team of 8, San Francisco, Winter 2026 batch; the President leads the inference service and customer onboarding; one open job listed on YC.Source
• PressTekedia Capital investment note (March 18, 2026): a platform pairing photonic hardware with its own software stack to break the "memory wall", claiming performance comparable to traditional compute clusters at lower cost; advisors include the Director of MIT's Research Laboratory of Electronics and a former Groq executive.Source

Release one self-contained component, such as a KV-cache tiering or offload module that runs on standard GPUs, under an open licence, with benchmarks and a note on how it runs faster on Piris Labs optics. Present it at inference meetups and in a technical post.

Open-source work reaches the inference engineers who choose providers, recruits engineers for a team that needs them, and shows the software depth behind the hardware claims.

• Maintenance load

  Release one narrow component and set clear support expectations.

• Giving away an edge

  Keep hardware-specific paths closed.

• HiringFounding Engineer, AI Inference Stack (San Francisco, in person, $100K-$200K, 0.10%-1.00% equity, new grads OK): orchestrate inference across heterogeneous compute clusters; write kernels in CUDA or Triton; build a low-latency inference server in the vein of vLLM or TensorRT-LLM, with KV cache management that avoids PCIe bottlenecks; named focus areas are instant-on voice AI and memory management for coding agents.Source
• YC profileTagline "The Networking Layer of AI"; says up to 80% of advanced AI compute chips sit idle because of slow networking; founded 2025, team of 8, San Francisco, Winter 2026 batch; the President leads the inference service and customer onboarding; one open job listed on YC.Source