We stand up open-weight language models inside your institution — fine-tuned on your data, served from your hardware, air-gapped if you need it. Ask questions of your own documents, extract from your own forms, transcribe your own calls. The data never leaves the building.
Inference and fine-tuning run inside your boundary — on-prem or air-gapped
Sending patient records, client files, case papers or student data to a third-party API means your most regulated information is processed on infrastructure you don't control — a governance and sector-compliance problem under the DPDP Act 2023 and rules from RBI, ICMR and others.
Per-token pricing is unpredictable by design: the more your institution uses AI, the more you pay, every month, forever. For steady, high-volume internal workloads that meter turns into an operating cost that grows exactly when the tool becomes useful.
An off-the-shelf model has never seen your circulars, your product catalogue, your case history or your internal vocabulary. It answers in generalities where you need it to answer in your own documents, formats and terminology.
We don't resell someone else's API. We deploy open-weight models on hardware you own, tuned on data you keep, so the intelligence lives where your data already lives.
Inference and fine-tuning run inside your network — on-premise or in a private cloud you control, air-gapped if your policy demands it. No prompt, no document and no training example is sent to an outside API.
We build on open-weight families — Llama, Mistral, Qwen, Gemma, DeepSeek — chosen per your requirement. You hold the weights and the adapters. There is no proprietary model you can be cut off from or repriced on.
Using LoRA/QLoRA (PEFT), we adapt a base model to your documents, formats and vocabulary — so it answers in your terminology and your house style, not in generic prose.
You pay for hardware and a defined engagement, then run it. Internal usage can grow without a per-token meter running against it — the economics favour steady, high-volume institutional workloads.
A full private-AI capability, from choosing the right model to running it in production on your infrastructure.
An open-weight model served on your hardware with vLLM, Ollama or llama.cpp — behind your firewall, optionally air-gapped
Parameter-efficient tuning on your own domain data, producing a small adapter that stays entirely with you
Retrieval-augmented answers grounded in your files with citations, using pgvector or Qdrant — the model reads your library, not the internet
An internal chat interface your staff can use over your own knowledge — no data leaves, role-based access, full audit trail
Turn scanned forms, invoices, contracts and records into structured, queryable data — processed on your own servers
On-premise transcription with Whisper-family models for calls, dictation and meetings — recordings never leave your network
An honest, task-specific evaluation on your own data so the model you deploy is chosen on measured results, not marketing
Right-sized hardware guidance — which cards, how much VRAM, how much quantisation — so you buy what your workload actually needs
Monitoring, evaluations, model updates and retraining as your data grows — with Docker/Kubernetes packaging and runbooks
Every engagement leaves you with running software and the artefacts, documentation and knowledge to operate it yourself.
A production model endpoint on your own hardware
Your domain, learned into a portable file you own
Answers grounded in your documents, with sources
Evidence, not a slogan, behind the model choice
Everything your team needs to run it without us
Your people learn to own the system
A grounded, four-stage engagement. Nothing goes to production until it clears an evaluation you approve.
We map the use case, the data you have, your regulatory constraints and your GPU budget — then agree an evaluation set that defines what "good enough" means for you.
We pick the right open-weight model on measured results and, where it helps, fine-tune it with LoRA/QLoRA on your data — training on infrastructure you control.
We serve the model on your hardware with vLLM or Ollama, wire up RAG or extraction as needed, and can run the whole stack air-gapped if your policy requires it.
We set up monitoring and evals, hand over runbooks and training, and — if you want it — stay on for retraining and updates as your data and needs grow.
Hosted APIs are excellent and often the right call. This table is not to dismiss them — it's to show precisely where a private model earns its place, so you can draw the line for your own data.
| Consideration | Private, on-premise AI | A hosted API |
|---|---|---|
| Where your data is processed | On your own servers — never leaves | Sent to the provider's network |
| Cost model | Fixed capex + defined engagement | Per-token opex that scales with use |
| Model stability | The exact weights you deployed, frozen | Provider can update the model under you |
| Air-gapped / offline operation | Possible — no internet required | Requires outbound connectivity |
| Who owns the fine-tuned adapter | You do — it stays on your storage | Tuning lives inside the provider |
| Time to get started | Setup, sizing and deployment first | An API key and you're running |
| Peak capability on hardest reasoning | Strong, bounded by your hardware | Largest frontier models still lead |
| Best fit | Regulated, sensitive, high-volume data | Public, non-sensitive, bursty tasks |
No proprietary black box. We build on the open-weight and open-source stack the field actually runs on, plus the application stack we've shipped for years — so the whole system is inspectable and yours to keep.
Every engagement is scoped to your data, use case and GPU footprint — so we quote after a short discovery call, never from a menu. Tell us what you're trying to do and we'll come back with a plan.
Assess feasibility & fit
Prove it on your data
Live on your infrastructure
Ongoing MLOps & support
Because the model runs on your hardware, it can be deployed with no route to the internet at all. Nothing is uploaded, nothing is metered, nothing phones home — a fit for defence, government and any air-gapped estate.
We build on open-weight models and hand you the base weights and the LoRA adapter we train. There is no proprietary endpoint that can be switched off, repriced or quietly changed under you.
Once the hardware is in place, internal usage doesn't tick a per-token meter. Teams can query, extract and transcribe as much as the work demands without an invoice that grows with adoption.
It depends on the task. For many institutional workloads — answering questions over your own documents, extracting fields from forms, classification, drafting in your house style, transcription — a well-chosen open-weight model in the Llama, Mistral, Qwen, Gemma or DeepSeek families, fine-tuned on your data, is genuinely competitive and often good enough to ship. On the hardest open-ended reasoning, the largest hosted frontier models still lead. Our honest job is to measure your actual use case with an evaluation set you approve, and only recommend on-premise where the results clear your bar. Where they don't, we will tell you.
It is driven by the model size, the number of concurrent users, and how fast responses must feel. A quantised 7–8B model can serve a small team on a single 24 GB card such as an RTX 4090 or an L4; 70B-class models and heavier concurrency need 48–80 GB cards like the L40S or H100, sometimes several. We size this in the assessment phase, use quantisation (GGUF, AWQ) to fit more model onto less VRAM, and give you a written spec so you can procure the right hardware — or start on rented GPUs and move on-prem later.
No language model can be guaranteed never to produce a wrong or invented answer, and we will not claim otherwise. What we do is reduce it and make it visible: RAG grounds answers in your actual documents with citations back to the source passage, we tune prompts and retrieval so the model prefers 'I don't know' over guessing, and we build an evaluation set so you can measure the error rate on your own data rather than trust a slogan. For high-stakes outputs we keep a human in the loop by design.
No. Fine-tuning runs on infrastructure you control — your own servers, or an isolated environment you own and can air-gap. Your training data is never sent to a third-party API and is never used to train anyone else's model. The output of LoRA/QLoRA training is a small adapter file that belongs to you and stays with you. If your policy requires it, we do the entire process inside your network under your supervision.
The DPDP Act 2023 makes you, the Data Fiduciary, accountable for personal data you process — including data you hand to a processor. When inference and training run entirely on your own servers, personal and sensitive data never leaves your controlled environment, which materially shrinks the surface you have to govern, contract for and audit. It supports data-residency and purpose-limitation obligations and keeps sector rules — RBI, ICMR, CERT-In and others — inside your own boundary. We are engineers, not your legal counsel, so we build the technical controls; your compliance team owns the final assessment.
Then you may already have the right tool for part of your work. Hosted APIs are excellent, fast to start with, and a sensible choice for non-sensitive workloads. Our pitch is narrow and specific: the subset of your data that must not leave the building — patient records, client files, case papers, financials, exam or defence material. Many institutions run a hybrid: hosted APIs for public, low-risk tasks and a private on-premise model for regulated data. We are happy to help you draw that line honestly rather than move everything.
If your institution holds data that shouldn't leave the building, let's talk about a private model that runs where it lives. Start with a short assessment — no obligation, and an honest answer on whether on-premise is right for you.
Tell us about the data and the use case. We'll come back with an honest view on whether a private, on-premise model fits — and what it would take to stand one up on your infrastructure.