Capability · Open Weight Models
Frontier-Class AI, on Your Infrastructure
Fine-tuned open models served in your cloud - for the workloads where data can't leave, per-token pricing doesn't scale, or control is non-negotiable. Promoted only after beating the API baseline on your evals.
Trusted by 100+ founders
Residency
PHI, financial records, IP - data that can't leave your cloud doesn't have to. The model comes to the data.
Unit Economics
At sustained volume, owned inference beats per-token pricing - and the cost curve is yours to engineer.
Control
No deprecations, no silent model swaps, no rate limits. Your weights, your latency, your roadmap.
The Pipeline
Everything Inside Your Boundary
Base model in, fine-tune on domain data, eval against the frontier baseline, serve with vLLM. Nothing crosses the dashed line.
How We Build It
The Parts That Make It Work
Open weights are free; production-grade open-weight systems are not. This is the discipline behind ours.
01
Build-vs-License Evals
The decision is empirical: benchmark open models against frontier APIs on your task. Open weights ship only when they win.
02
Fine-Tuning
LoRA and QLoRA adaptation, DPO on preference data, domain vocabularies - a 70B tuned on your data beats a generalist on your task.
03
Serving & Quantization
vLLM with continuous batching, AWQ/GPTQ quantization, and GPU right-sizing - throughput engineering, not just hosting.
04
Eval-Gated Releases
The same harness that picked the model gates every update. Regressions are caught in CI, not by users.
05
Compliance & Residency
VPC and on-prem deployment patterns for HIPAA and SOC2 environments - including fully air-gapped.
06
Distillation & Cost
Big teacher, small student: distill expensive reasoning into small models where latency and cost demand it.
Deployed With
LlamaMistralQwenGemmavLLMHugging FaceAxolotlTensorRT-LLMThe Team Behind It
You Get Engineers, Not Tickets
100+ full-time product people - engineers, designers, PMs, and QA - led by founders who've built, scaled and exited their own startups. Senior people are on your product from day 1, working your hours. This is the same team that fine-tunes and serves models inside client clouds.
Meet the team
Rahul Nair
Co-Founder & Head of Engineering
Architect behind every AI system we ship to production.
+100Full-time team · 0 freelancers · US-hours overlap
Testimonials
Founders on Working With Tequity
Pre-Seed to Series B
“We hired Tequity shortly after closing our pre-seed, and since then they've completely taken over our frontend and DevOps work. Typical turnaround is 1 day for critical bug fixes, 7 days for new features, and 6 weeks for entire MVPs. The software we built together is now used by multiple leading American biopharma companies. I recommend Tequity for any startup from angel round through Series B and beyond.”
Founder & CTO, TerraFlow
“The best outsourced engineering help you can get. Genuinely talented engineers who deliver on time, take full ownership of the product, and push back on your ideas until they understand why you're building each thing.”
Founder, Bantor
4.9/5
Average rating across 100+ customers over 4 years
“They helped us audit our product, identify key UX/UI improvement opportunities, and prioritize quick wins that delivered immediate value. The team has been responsive, collaborative, and consistently delivers high-quality work.”
Founder & CEO, Boxsy
The work behind the words.
See all case studies →
Co-Founder, QuantWheel
“We came in expecting a redesign and got something more useful. Design Labs built us a design system our AI could actually build against, so what we ship stays consistent without us having to think about it. We'd give feedback and see it reflected in the next round, often the same day.”
LET'S TALK
Need AI Where Your Data Lives?
Book a 30-minute call. We'll scope the build-vs-license question for your workload - with numbers, not opinions.