Skip to content

Our approach

Measure first. Train the smallest thing that works.

Every engagement follows the same six-stage pipeline, and no method is chosen before the numbers are in.

The pipeline

Six stages from idea to improving model

  1. 01

    Scope

    Pick one high-value task, define what a good result looks like, and measure the baseline the new model has to beat.

  2. 02

    Data

    Collect, clean, and label examples from your systems. PII is scrubbed before anything touches a GPU.

  3. 03

    Train

    Use the lightest method that meets the target: adapters first, full training only when the numbers show it's needed.

  4. 04

    Evaluate

    Candidates compete head to head. Only a measurable winner moves forward, and the release checklist is set out below.

  5. 05

    Deploy

    Serve the winning model in your environment, behind an API your applications already know how to call.

  6. 06

    Improve

    Monitor live traffic, collect the cases the model gets wrong, and feed them into the next training round.

Decision guide

Fine-tune, RAG, or build from scratch?

Six ways to customise a model, ordered from lightest to heaviest. Most teams need one of the first three.

Prompting + RAG

Use it when: Your knowledge changes often and the base model already reasons well about your domain.

The fastest way to start, but per-call cost and latency stay high and the output style is hard to pin down.

Data needed
Your documents. No labelled examples required.
Typical timeline
1–3 weeks

LoRA / QLoRA

Use it when: You need consistent formats, tone, or task accuracy, and you have a few thousand good examples.

Cheap to train, and small adapters can be swapped on a shared base model. On hard tasks it tops out below full fine-tuning.

Data needed
1k–20k instruction examples
Typical timeline
2–4 weeks

Full fine-tuning

Use it when: Adapters have plateaued and the task is valuable enough to justify updating every weight.

The best task accuracy for a given model size, but it takes more GPU time and you host a full model.

Data needed
20k+ high-quality examples
Typical timeline
4–8 weeks

DPO / RLHF

Use it when: The model is capable, but its judgement (helpfulness, refusals, tone) doesn't match your experts'.

Changes how the model behaves, not what it knows. It depends on well-calibrated reviewers.

Data needed
Preference pairs or ranked outputs from trained reviewers
Typical timeline
3–6 weeks

Continued pretraining

Use it when: Your domain's language, such as legal, clinical, or a regional language, is under-represented in public data.

Gives the model deep fluency in your domain. It needs a larger compute budget and instruction tuning afterwards.

Data needed
Hundreds of millions of domain tokens
Typical timeline
6–10 weeks

Train from scratch

Use it when: You need a small model with a custom tokenizer, strict licensing, or tight on-device limits.

Full control over the architecture and licence. This is the largest investment.

Data needed
Billions of tokens, often partly synthetic
Typical timeline
3–6 months

Release checklist

How we decide a model is ready

Every model we ship passes the same checks, and you get the evidence, not a summary.

  • Test sets come from real traffic and are never used for training
  • Automated graders are checked against human reviewers before we trust them
  • The base model is scored on the same set as the tuned model
  • Every candidate goes through a safety and red-team suite
  • Latency and cost per 1K requests are reported next to accuracy
  • Your domain owner signs off on the results before release

Find your starting method

A 30-minute conversation is usually enough to rule methods in or out.

We reply within one business day (IST).

Talk it through (opens WhatsApp in a new tab)