AI & Intelligent Systems
LLM systems built on your knowledge, accountable to your standards
RAG platforms, copilots, and document intelligence built on your data, inside your boundaries.
Generic chatbots don't know your policies, your contracts, or your customers. The value is in AI grounded in your data — but that demands retrieval engineering, evaluation, and data-boundary design that wrapper products skip.
- Answers grounded in your data, with citations your auditors can follow
- Hours of reading, drafting, and checking compressed to minutes
- Model and vendor flexibility behind one abstraction layer
Capabilities
What the work actually involves
RAG knowledge platforms
Retrieval over your documents, wikis, and systems of record — with citations, permissions, and freshness pipelines.
Enterprise copilots
Assistants embedded in the tools your teams already use, drafting, summarising, and answering with your context.
Document intelligence
Extraction, classification, and verification across invoices, KYC, contracts, and claims at production accuracy.
Fine-tuning & model adaptation
Domain-adapted models where prompting hits its ceiling — with training data pipelines you own.
Evaluation harnesses
Regression suites for AI behaviour: accuracy, safety, and cost tracked release over release.
Ingestion and freshness pipelines
Event-driven updates when a source document changes, effective-date awareness so superseded versions lose rather than compete, and deletion that actually removes the vectors.
Model abstraction layer
One interface over whichever model is behind it, so a price change, a deprecation or a better option next quarter is a configuration decision rather than a rebuild.
Deliverables
What you are handed.
Yours to keep, and written so another team could pick them up.
A working system in your environment
Deployed inside your boundary or on a zero-retention tier, not a hosted demo we control.
A scored evaluation set
The tests that say whether a change made it better. Without this, tuning is superstition.
Data-boundary documentation
What leaves, what never can, what is redacted — written down for your security review.
Cost model per transaction
Token and infrastructure spend per unit of work, so the finance case survives scale.
We do not publish prices — scope drives them. Everything else, here.
- Starts with
- A two-week feasibility spike against your real data
- Typical duration
- 8–16 weeks to production
- Who you get
- Two AI engineers, a platform engineer, an architect
- Commercial model
- Fixed-scope spike, then phased build
How to decide
What the answer depends on.
Two sets of conditions. Read both against your own situation — most organisations recognise themselves in one column within a sentence or two.
This is the right call when
- You have documents or knowledge that a generic model knows nothing about.
- Answers must be traceable to a source, not merely plausible.
- Your data cannot go to a public endpoint without controls.
A different approach fits better when
- A general-purpose chatbot over public content. An off-the-shelf product is cheaper and good enough for that.
- You have no evaluation criteria and do not want to define any.
- Nobody internally can adjudicate whether an answer is correct.
Proof
Where we have done this
AI-assisted loan origination for a fast-scaling NBFC
An AI-assisted origination platform that reads documents, flags risk, and routes applications — approval turnaround down 68% with tighter, not looser, controls.
68%Faster approval turnaround
AI career counselling and college matching
Students talk to an AI for career counselling and find the colleges that fit them. Built in 2023 as three connected pieces: the web experience, the mobile app and the portal behind both.
Before you ask
Questions about custom AI & LLM development
Sectors
Where this comes up most.
The regulatory context and the systems already in the building change the build. Each sector page says how.
Next step
Bring us the problem. We will bring the architecture.
A discovery call takes forty-five minutes. You leave with our read on the problem, the shape of the system we would propose, and a straight answer on whether we are the right team for it.
- No sales deck
- An engineer on the call, not an account manager
- NDA before you share anything