DEEP-TECH AI · BUILD & TRAIN
AI that knows the domain.
Not a wrapper around someone else's model. A model we trained on your field, an application built by people who understand the work, and the evaluation harness that proves it holds up.
institutions where we've taught AI
students and faculty trained
models trained and shipped
sectors with production deployments
IIT · VJTI · NMIMS · ST. XAVIER'S · + 46 MORE
BUILD
Three things, done properly.
We don't do generic. Every engagement produces an artefact you own — weights, code, evals, and the documentation to maintain it without us.
Specialised models
Fine-tuned and purpose-trained models for narrow, high-stakes tasks where a general model guesses and a specialised one knows.
- Domain LLMs — QLoRA / full fine-tunes on your corpus, your terminology, your compliance language
- Vision and multimodal — defect detection, document intelligence, medical and industrial imaging
- Retrieval and ranking — embeddings trained on your data, not on the open web
- Distillation — take a frontier-model capability and compress it into something you can afford to run at volume
- Evaluation harnesses — the part everyone skips. You get a test set, a scoreboard, and a regression gate.
model weights · eval report · inference container · retraining runbook
Domain-native applications
Applications built by engineers who took the time to learn how the job is actually done.
- Workflow tools that fit the real process, including the exceptions nobody documented
- Human-in-the-loop by default — confidence thresholds, escalation paths, audit trails
- Deployed inside your perimeter: on-prem, VPC, or air-gapped
- Integrated with what you already run, not a parallel system your team has to remember to open
source code · infrastructure-as-code · runbook · handover to your team
Deep-tech R&D
For problems that don't have an off-the-shelf answer yet.
- Applied research engagements with a defined question and a defined stopping condition
- Architecture and feasibility reviews before you commit a year of budget
- Model risk assessment — where it fails, how badly, and what it costs when it does
- Co-development with your research team, structured so the capability stays with you
technical report · prototype · go / no-go recommendation with the reasoning shown
DOMAINS
We only take work in fields we can learn properly.
Domain understanding is not a discovery call. It's weeks with your operators, your edge cases, and your failure logs — before a single line of training code.
| Domain | What the model has to understand | Representative work |
|---|---|---|
| Advertising & media | Brand safety lines, tone systems, campaign taxonomies, what a creative director will actually approve | Creative generation and QA inside agency workflows |
| Banking & capital markets | Regulatory language, product hierarchies, the difference between a complaint and a grievance | Document intelligence and operations automation |
| Education & assessment | Rubrics, learning outcomes, how a grader thinks, what “partially correct” means | Assessment, feedback, and curriculum systems |
| Retail & consumer | Catalogue structure, substitution logic, returns behaviour, what customers actually type | Ranking, personalisation, and support automation |
| Manufacturing & industrial | Tolerances, defect classes, shift patterns, what a false positive costs the line | Vision inspection and process intelligence |
| Public sector & skilling | Programme structures, accreditation requirements, scale and language constraints | Curriculum delivery and evaluation at national scale |
APPROACH
Five steps. No black boxes.
This is the only sequence on this page, so it's the only place you'll see numbers.
- 01
Immersion
We sit with the people who do the work. We read the failure logs, not the pitch deck. Two to three weeks, fixed fee, and you keep the findings whether or not we go further.
- 02
Data and feasibility
What data exists, what's usable, what's missing, and whether the problem is tractable at all. If the honest answer is “don't build this yet”, we say so — and we've said it before.
- 03
Baseline and evals
Before we train anything, we build the scoreboard. A held-out test set, human-graded where it matters, and the number a general-purpose model scores on it. That number is what we have to beat.
- 04
Train and iterate
Fine-tuning, distillation, retrieval, or architecture work — whatever the eval says the problem needs. You see the scoreboard move every week. No surprises at the demo.
- 05
Deploy and hand over
Inside your perimeter. Your team gets the weights, the code, the retraining runbook, and enough training to run it without calling us. If we've done this right, you don't need us for maintenance.
TRAIN
We teach AI in 50+ institutions. Including our own engineers.
The people who build the models teach the courses. That's the whole model. Curriculum comes out of live engineering work, gets taught in colleges and enterprises, and the questions we can't answer well go back into the engineering.
Faculty development
AICTE-aligned programmes that get teaching staff to the point where they can build, not just describe. Delivered on campus or online, with a capstone that produces something real.
Student cohorts
Semester-length and intensive formats. Fundamentals through fine-tuning and deployment. Assessed on shipped work, not attendance.
Corporate upskilling
For engineering, product, and risk teams. Grounded in your stack and your data, so what's learned on Friday is usable on Monday.
Institutional AI labs
Curriculum, hardware specification, project pipeline, and industry linkage. We help you stand up a lab that keeps running after we leave.
An AI course that has never shipped a model is a literature review.
STANDARDS
The unglamorous commitments.
DATA STAYS PUT
Training and inference run inside your VPC, on-prem, or air-gapped. Your data does not leave your perimeter, and it never enters a third-party training set.
YOU OWN THE WEIGHTS
Model artefacts, code, and evals are yours, in writing, from day one. No licensing trap. No hostage maintenance contract.
EVALS BEFORE DEMOS
We show you the scoreboard before we show you the demo. A demo can be cherry-picked. An eval set can't.
WE SAY NO
If the data isn't there, or a rules engine would do the job for a tenth of the cost, that's what we'll tell you. We'd rather lose the project than ship something that quietly fails in production.
AUDITABLE BY DESIGN
Every inference is logged with its inputs, confidence, and the version of the model that produced it. When a regulator asks, you have an answer.
BUILT TO BE HANDED OVER
The engagement ends with your team running it. Documentation, runbooks, and training are deliverables, not afterthoughts.
QUESTIONS
The things people ask before they call.
Why train a model when I could just call an API?
Often you should — and we'll tell you when. Call an API when the task is general, the volume is low, and the cost of a wrong answer is small. Train a model when the vocabulary is specialised, the volume makes per-token pricing painful, the data can't leave your network, or a general model is confidently wrong in ways your team can't catch.
How long does an engagement take?
Immersion and feasibility: 3–5 weeks. A first production model with an eval harness: typically 10–16 weeks after that. We'll give you a range with the reasoning behind it, not a number designed to win the deal.
Can you work inside our security perimeter?
Yes. On-prem, VPC, or fully air-gapped. Several of our deployments have no external network route at all.
What happens to our data?
It stays yours and it stays where it is. It is never used to train anything for another client, and it never leaves your infrastructure without written instruction. This is in the contract, not just on the website.
Do you do training and consulting for institutions?
Yes — it's half of what we do. 50+ colleges, AICTE-recognised programmes, faculty development and student cohorts. Same engineers, same material.
What's the smallest engagement you'd take?
An immersion or feasibility study. It's fixed-fee, it's short, and you keep the output regardless of what happens next. That's the right way to find out if we're the right team.
CONTACT
Tell us what you're trying to build.
A real engineer reads every message. You'll hear back within one working day — with a view, not a calendar link.