LLM solutions tuned to your domain — not generic.
Retrieval pipelines, fine-tuning, prompt engineering and rigorous evaluations — designed for the data, vocabulary and risk profile of your business.
A general-purpose model is a starting point, not a solution.
An off-the-shelf LLM doesn’t know your products, your tone, your industry rules or your private data. Closing that gap — reliably, cheaply, securely — is what an LLM solution actually is.
We architect the right combination of retrieval, prompts, structured outputs, fine-tuning and evaluation for your problem. The goal: answers that are accurate, fast, on-brand and provably better than a baseline.
- Retrieval-augmented generation (RAG)
- Fine-tuning & instruction tuning
- Prompt engineering & templating
- Embedding pipelines & vector search
- Reranking & hybrid search
- Evaluation suites & observability
- Self-hosted & on-prem deployments
- LLM cost & latency optimisation
Where LLM systems live or die.
Retrieval (RAG)
Chunking, embeddings, vector + keyword hybrids and rerankers tuned for your content.
Fine-tuning
Instruction, preference and lightweight fine-tunes when prompting alone isn’t enough.
Prompt engineering
Versioned prompts, few-shot strategies, structured outputs and tool-calling patterns.
Evaluations
Golden sets, rubric scoring, regression alerts and A/B comparisons across models & prompts.
Cost & latency
Smart model routing, caching, distillation and right-sizing — without hurting quality.
Privacy & controls
PII redaction, data residency, retention policies and self-hosted options for sensitive deployments.
From idea to a measured production system.
Define the task
Inputs, outputs, success criteria, accuracy target. We focus on a sharp, evaluable task — not vibes.
Build the eval
A versioned test set with realistic inputs and graded expectations — the foundation of everything.
Iterate the system
Try retrieval, prompts, models, fine-tunes. Measure each change. Keep what wins, throw out what doesn’t.
Ship & observe
Roll out behind feature flags, watch usage and costs, capture feedback, regress against the eval suite.
What teams build with LLMs.
Knowledge assistants
Internal “ask anything” assistants grounded in policies, contracts, product docs and tickets.
Document understanding
Extract, classify and summarise long documents with verifiable accuracy.
Domain-specific copilots
Legal, medical, financial or operational copilots tuned to your terminology and rules.
Smart product features
Search, suggestions, drafts, replies, summaries — embedded into existing product flows.
Reporting & insights
Generate human-readable insights from structured data — sales, ops, risk — on demand.
Multilingual workflows
Translation, transliteration and multilingual support for global users and content teams.