AI that reaches production — not just the demo.
We design, build, and ship AI systems for mid-market teams: production RAG, fine-tuned models, and LLM features inside your product. Deployed in your cloud, measured with real evals, and handed to your team to own.
10+ yrs shipping production software in big tech — fintech & e-commerce
Deployed in your cloud — your data stays inside your walls
Evals with every build — measured, not guessed
Senior hands only — the people who scope it are the people who build it
Three ways in, one standard of work.
Most engagements start small — an audit or a pilot — and grow into a build once the value is proven. Wherever you start, you get senior engineers on the problem and software your team can maintain.
Audits & Roadmaps
Know what to build, and what it's worth, before you spend on the build.
- AI Readiness Sprint — two weeks, fixed price: systems and data mapped, opportunities ranked, a go/no-go. See the Sprint →
- LLM Cost & Quality Audit — evals, cost per query, latency, and the specific fixes to reclaim wasted inference spend.
- Technical due diligence — a straight read on an AI system, team, or vendor.
Systems & Products
The core work: AI that runs in production and survives real users and real data.
- Production RAG systems — ingestion, retrieval tuning, evals, guardrails, deploy.
- Private fine-tuned models — open models tuned and aligned to your task, in your VPC.
- LLM features in your product — copilots, semantic search, summarization.
- Agentic workflow automation — intake, triage, and document processing.
Training & Support
So the capability stays with your team after we hand off.
- Team training workshops — hands-on RAG-in-production and fine-tuning, on your stack.
- AI Ops & maintenance — eval monitoring, drift, cost optimization, model updates.
- Fractional AI lead — senior direction without a full-time hire.
A short path from problem to shipped.
No long procurement cycles or bloated statements of work. We diagnose fast, scope tightly, and keep you informed every week.
Intro call
A focused conversation about what you're building and where it's stuck. By the end we both know if there's a fit — and if there isn't, we'll say so.
Scope & proposal
We confirm the problem in writing, then send clear options with a recommendation: fixed scope, fixed price, defined milestones. Usually within 48 hours.
Build
Senior engineering on the problem, evals throughout, and a written status update every week without you having to ask.
Handoff & support
Documentation, a working system, and a team trained to run it. Ongoing AI ops available when you want it.
Eval targets, agreed up front.
Before we build a pilot, we define the metrics that decide success with you — retrieval accuracy, cost per query, latency, whatever matters for your use case. If the pilot doesn't hit the targets we set together, you don't pay the final milestone.
Applies to fixed-scope pilots with pre-agreed eval targets; excludes changes you request mid-flight.
Why teams choose Vychara.
Product and ML under one roof
We build the model and the product around it. That's usually where these projects stall: the ML works in a notebook but never ships. We deliver both.
Private by default
Fine-tuned open models deployed in your cloud. Your data stays inside your walls, and inference often costs a fraction of frontier APIs on your specific task.
Evals before opinions
Every recommendation ships with a measurement. If a claim can't be measured, we'll tell you — including when AI is the wrong tool for the job.
Depth below the API line
We've built language models end to end — tokenizer to alignment. You almost certainly don't need one built that way, but knowing how the machine works is the difference between tuning a system and guessing at it.
Led by an engineer who ships.
Neel Mistry — Founder & Principal
Ten-plus years building and shipping production software in big tech — fintech and e-commerce — plus deep, hands-on work in language models end to end: tokenizer, fine-tuning, alignment, and RAG. The person who scopes your project is the person who builds it. No junior hand-off after the sale.
Full background on LinkedIn →What you'd do instead — and the trade-off.
Every mid-market AI project has three obvious alternatives. Here's the honest version of each.
| The alternative | The trade-off teams hit | With Vychara |
|---|---|---|
| Large / Big-4 consultancy | Real depth, but slow, priced for their overhead — and you often get juniors after the pitch. | Senior depth without the enterprise overhead, scoped in weeks. |
| Offshore dev shop | Cheap and fast, but usually wraps an API without the ML underneath. The demo works; the product stalls. | Real ML and product engineering, built to survive production. |
| DIY (ChatGPT + one hire) | Fine to prototype, but one generalist rarely gets a private, evaluated system all the way to production. | A shipped system your team owns, measured with evals. |
How we think about shipping AI.
No client case studies to name yet — so here's the thinking instead. Field notes on getting AI from demo to production.
The questions mid-market teams actually ask.
Is our data safe?
Everything runs in your cloud or VPC. Your data doesn't leave your walls, isn't used to train anything, and is deleted on your schedule. Private deployment is the default here, not an upsell.
We don't have clean data or an ML team — can we even do this?
That's exactly what the AI Readiness Sprint is for. We map what you have, tell you honestly what's usable, and rank what's worth doing before you commit to a build.
Will we be locked into you?
No. You own the code, the documentation, and the model weights, and we train your team to run it. We're built to hand off — removable by design.
We can't get budget approved without a number.
Start with the fixed-price AI Readiness Sprint. Builds are quoted after, tied to defined milestones, so you always know the number before work begins.
How fast is this?
The Sprint is two weeks. A typical RAG pilot is about 30 days to a go/no-go. We diagnose fast and scope tightly — no six-month statements of work.
What if AI is the wrong tool for our problem?
We'll tell you, and the evals will show it. You'd rather hear that in a two-week Sprint than after a six-figure build.
Tell us what you're trying to ship.
Bring the problem and where it's stuck. In one call you'll get a straight read on whether AI is the right move, what it would take, and how we'd approach it. If we're not the right fit, we'll point you somewhere better.