Ops, support, and data leaders bring us a workflow that burns time or money. We design, build, and run the AI system that fixes it. We stay on the hook for results after launch. Working products, not research papers.
Free, 45 minutes. You leave with a written feasibility read — whether or not we work together.
Founder-led, nothing outsourced. Every number is measured against a pre-launch baseline; the method behind each is in the case studies.
Four shapes of AI problem we take end-to-end: strategy, build, deploy, operate. If your problem looks AI-shaped, we should talk.
Hand off the work that moves between your systems, without adding headcount. Agents connect to your databases, tools, and SaaS apps, act on what they find, and escalate to a human where it counts.
Find out what your customers and employees are actually telling you. We process surveys, transcripts, tickets, and calls at scale, then hand stakeholders the briefs and dashboards they'll actually read.
Cut your model bill and keep your data inside your own walls. We deploy and run open-weights models on your infrastructure, with routing, autoscaling, and monitoring included.
Most problems don't fit a neat category. If it can be solved, or made meaningfully better, with AI, that's our favorite kind of project.
No discovery-call theater, no open-ended hourly billing. Four steps, each with a fixed shape and a concrete deliverable.
You describe the problem. We tell you honestly whether AI fixes it, and roughly what that costs.
You get: a written feasibility read.
We look at your data, systems, and constraints before promising anything.
You get: an architecture, an eval plan, a fixed quote.
We ship into your infrastructure, with evals gating every release.
You get: a working system, demoed to you weekly.
Versioning, regressions, capacity planning, cost control. Or a full hand-off package if you would rather run it yourself.
You get: weekly reports your CFO will actually read.
Good fit: you have a real workflow that costs you money and the data it runs on. Bad fit: you want a strategy deck about AI. We don't write those.
Every engagement runs the same way: a scoping call, a fixed-scope build measured in weeks, then production with us on the hook for uptime, cost, and quality.
We run your models on your infrastructure: GPU fleets, request routing, autoscaling, monitoring. One client now pays 78% less per million tokens than the API equivalent.
Production agent loops with evals, guardrails, and human review where it counts. Built for the support and ops queues that are eating your headcount.
We run what we ship: uptime and latency SLOs, eval-gated model updates, and a monthly cost review. A full hand-off package is standard if you want it in-house.
Not sure which of these you need? That's what the scoping call is for.
The voice agent on this site handles support calls in Hindi, Tamil, and English, switching mid-sentence when the caller does. It answers in under a second and hands off to a human when it should.
The receipts behind the numbers we quote. Short, technical write-ups of what we're shipping, including the parts that took us longer than they should have.
Latency budgets, barge-in, and code-switching across Hindi, Tamil, and English — written from the reference build that runs live on this site.
Read entryA walkthrough of the routing, batching, and quantization decisions we use to run Qwen3.5 fleets on client GPUs without blowing budget.
Read entryHow a small pipeline of classifiers, clustering, and LLM summarization turned a year-end HR survey into a 6-page brief leadership actually read.
Read entryThe scoping call is free. Discovery runs two weeks at a fixed fee quoted up front; build phases are fixed-scope and quoted after discovery — typically low-to-mid five figures depending on integrations. You have the exact number before anything starts, and never an open-ended hourly bill.
You do. Code lands in your repos, models and weights live in your accounts, and your data never has to leave your infrastructure. Sovereign deployment is one of our core services, not an add-on.
We agree on the accuracy bar during discovery and build the evals before the features. A system that fails its evals doesn't act; it escalates to a human. And if we don't believe we can hit the bar, we say so at the scoping call and you've spent nothing.
Yes. VPC, on-prem, or air-gapped. Case study 02 covers exactly this: open-weights models operated inside the client perimeter with zero bytes of egress.
Work with them. Your engineers get the repos, the runbooks, and a weekly working session. Hand-off is a standard deliverable, not an exit fee.
Two weeks to a validated plan. A production system typically follows in 8–12 weeks; the support-analytics build in case study 01 took 14 weeks end to end.
Have a different question? Ask a founder on the free scoping call — you get a straight answer either way.
Send a short note about the problem. Within two days a founder replies with how we'd approach it, how long it would take, and what it would cost.