A demo is easy.Production is the hard part.
Fine-tuning, retrieval and inference infrastructure built as one system — so the model that impressed everyone in a prototype survives real users, real data and real cost constraints.
Overview
What an LLM system actually needs.
The model is one component. Retrieval, evaluation, guardrails and the infrastructure underneath decide whether it holds up in production.
Custom LLM Fine-Tuning
Models adapted to your domain, terminology and tone on your own curated data — with dataset preparation and evaluation baked in, not bolted on afterwards.
Retrieval-Augmented Generation (RAG)
Your documents, tickets and records chunked, embedded and indexed so answers are grounded in your own knowledge base rather than the model's guesswork.
LLM Infrastructure & Hosting
GPU provisioning, model serving and scaling — self-hosted on our own infrastructure or API-based with a managed provider, chosen on workload and data-residency needs.
Prompt Engineering & Evaluation
Prompts versioned and measured against test sets, with guardrails for refusals, injection attempts and out-of-scope questions — behaviour you can regression-test.
Enterprise LLM Integration
Wired into the systems your teams already use — existing applications, ERP, CRM and internal workflows — with authentication and audit trails intact.
Multi-Model, Vendor-Agnostic Architecture
GPT, Claude, Llama and Mistral matched to the use case, behind an abstraction that lets you switch models as pricing and capability shift.
Outcomes
What an LLM build should actually deliver
Beyond a chat window: the operational changes a well-scoped LLM engagement is designed to produce.
Answers grounded in your own content
Retrieval over your documents, tickets and systems of record, with citations back to source, so responses can be checked rather than trusted blindly.
Hallucination treated as an engineering problem
Retrieval quality, prompt structure, output schemas and refusal behaviour are tuned against an evaluation set instead of being patched with instructions.
Evaluation before rollout
A graded test set built from real questions, run on every change, so you can see whether a prompt or model swap improved things or quietly regressed them.
Token spend that scales predictably
Routing, caching, context trimming and model tiering are designed in, so cost per conversation is understood before the system reaches full user load.
Sensitive content kept inside your boundary
Where confidentiality or residency requires it, open-weight models run on infrastructure you control, with the same retrieval and evaluation tooling around them.
A system your team can change
Prompts, retrieval configuration and evaluation sets live in your repository as reviewable artefacts, not as untracked strings inside a vendor console.
Not sure whether you need an LLM or just AI in your product?
This page is the deeper, LLM-specific specialization — models, retrieval, evaluation and the infrastructure they run on. Our Artificial Intelligence page covers the broader scope: AI features inside your software, intelligent workflows and decision systems.
See Artificial IntelligenceProcess
How an LLM engagement runs.
Prove value on a narrow use case, measure it honestly, then harden it for production.
- 01
Assess
Use case, data sources, accuracy expectations and privacy constraints documented — including an honest read on whether an LLM is the right tool at all.
- 02
Design
Model selection, retrieval strategy, hosting approach and evaluation criteria agreed up front, with cost per request estimated before build.
- 03
Build & Evaluate
Pipelines, prompts and guardrails developed against a held-out test set — scored on real examples from your business, not vibes from a demo.
- 04
Deploy & Monitor
Rolled out with logging, drift and quality monitoring, cost tracking and a clear path to retrain or swap models as the workload changes.
Tech stack
What the work is built on.
Selected per engagement — matched to your data sensitivity, latency budget and cost envelope.
Engagement model
How LLM engagements are structured
LLM projects go wrong when a prototype is mistaken for a product. We separate proving the use case from hardening it.
Use-case validation
A fixed-scope build of a working prototype against your real content, with an evaluation set and honest reporting on where quality holds and where it breaks. Ends with a recommendation on whether to productionise, restructure or stop.
Best for: First LLM initiative with an unproven use case.
Production build
The full system: ingestion and retrieval pipelines, model serving, guardrails, evaluation harness, observability, cost controls and integration into your applications, delivered to agreed acceptance criteria.
Best for: A validated use case heading for real users.
Private model deployment
Open-weight models deployed on your cloud or GPU infrastructure, including inference serving, scaling, fine-tuning workflow and the operational tooling around them.
Best for: Confidentiality, residency or unit-cost constraints.
Ongoing evaluation and tuning
A retainer covering retrieval tuning, prompt and model upgrades, regression testing against your evaluation set, and cost review as usage patterns change.
Best for: Live LLM features that need to keep improving.
Industries served
Where language models earn their keep.
The same architecture, tuned to very different vocabularies, risk profiles and compliance obligations.
- FinanceRBI/SEBI-regulated environment
- HealthcareDPDP & data-privacy focused
- GovernmentPublic-sector compliance
- ManufacturingOT/ICS-aware
- RetailPCI-DSS aware
- EducationStudent data-privacy focused
- HospitalityGuest-data & PCI-DSS aware
- StartupsBuilt for speed, not red tape
- EnterpriseAudit-ready, governance-first
FAQ
Questions we get asked about LLM projects
Straight answers on cost, data exposure, model choice and what happens after launch.
Let's take it past the prototype.
Tell us the use case and the data behind it. We'll come back with the model, retrieval and infrastructure approach that fits — and what it will actually cost to run.