AI & Data
LLM integration, RAG and evaluation for existing products
We embed LLM capability into products you already run — search, summarisation, classification, drafting — with the retrieval, caching and evaluation that keep it accurate and affordable.
40-70%
Inference cost reduction through routing and caching
99.9%
Feature availability with multi-provider failover
Typed
Structured outputs validated against schemas
CI
Prompt regression tests on every release
Definition
What llm integration means here
LLM integration is the engineering layer between a language model and your application: retrieval, prompt orchestration, tool calls, structured output validation, caching, fallback routing and evaluation.
Capabilities
What is included
Each capability is scoped, estimated and delivered against agreed acceptance criteria.
Retrieval augmented generation
Chunking strategy, hybrid search, reranking and citation rendering.
Model routing
Route by task complexity across providers and open models, with automatic failover.
Structured output
Schema-validated JSON with repair loops so downstream systems never break.
Caching & cost control
Semantic and exact caching, token budgets and per-tenant quotas.
Guardrails
Prompt-injection defence, PII redaction and policy filtering.
Evaluation in CI
Golden sets and regression gates that block quality drops before deploy.
Deliverables
What you receive
- Integration architecture and provider strategy
- Retrieval pipeline with citation support
- Prompt library under version control
- Evaluation harness wired into CI
- Cost and latency dashboards
- Runbook for model upgrades and incidents
Engagement models
How we can work together
Fixed-scope project
Defined outcome, milestone billing and a signed delivery plan. Best when requirements are stable and the business case is approved.
Dedicated squad
A cross-functional pod (architect, engineers, QA, delivery lead) reserved monthly for a rolling roadmap with sprint-level reporting.
Managed service / AMC
SLA-backed run and evolve model covering monitoring, incident response, security patching and a monthly enhancement allowance.
Implementation roadmap
How the engagement runs
Indicative timeline for a single-entity engagement; multi-country programmes are phased.
- 01
Assess · Week 1
Feature scope, data sources, latency and cost targets.
- 02
Retrieval · Week 2-3
Index, chunk, rerank and measure retrieval quality first.
- 03
Integrate · Week 4-5
Prompt orchestration, structured outputs and UI wiring.
- 04
Harden · Week 6
Guardrails, caching, failover, load and cost testing.
- 05
Operate · Ongoing
Monitor quality drift and re-baseline on model upgrades.
Comparison
Direct API calls vs an engineered LLM layer
| Concern | Direct API calls | Engineered LLM layer |
|---|---|---|
| Factual grounding | None | Retrieval with citations |
| Cost predictability | Poor | Budgets, caching, routing |
| Provider outage | Feature down | Automatic failover |
| Quality regressions | Found by users | Caught in CI |
Advantages
- Improves accuracy without changing your core product architecture
- Cuts inference spend materially at production volume
- Provider-agnostic, so model choice stays commercial not technical
- Regression gates protect quality across releases
Trade-offs to plan for
- Retrieval quality is bounded by your content quality
- Adds an operational surface that needs monitoring
- Very low-volume features may not justify the layer
Decision guide
Is this the right service for you?
Match your situation to the recommended starting point.
| If this sounds like you | We recommend |
|---|---|
| Answers must cite internal documents | RAG with reranking and citation UI. |
| LLM bill is growing unpredictably | Model routing, caching and budget enforcement. |
| Output feeds another system | Structured output with schema validation and repair. |
Industries
Where we apply llm integration
FAQ
LLM Integration questions, answered
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Often delivered together
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Ready to scope your llm integration engagement?
Share your context and we will come back with an approach, timeline and indicative investment — under NDA.