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LLM Integration
A language model doing a defined job inside your systems — measured, governed and costed.
Access to a model is not a capability. We integrate large language models into the products and workflows you already run — grounded in your own data, constrained to a defined job, evaluated against real examples, and costed per transaction before anything goes live.
What LLM Integration delivers
Grounded in your data
Retrieval over your own documents, records and systems, so an answer points at something real instead of being invented.
Structured output, not prose
The model returns validated fields your software can act on — the difference between a demo and a working integration.
Evaluated before it ships
A test set of real examples, scored on every change, so a new prompt or a new model cannot quietly make things worse.
Costed and capped
Cost per call measured, work routed to the cheapest model that passes, with limits and fallbacks so spend cannot run away.
Representative stack
OpenAIAnthropicLangChainLlamaIndexRetell AIAirtable
n8nCloudflare Workers AIGenerative, RAG and Agentic
Three different things \u2014 most businesses need more than one.
Fetching the relevant passages from your own content first, then asking the model to answer using only those.
When you need it: It is what separates an answer traceable to your documentation from a confident guess.
A fixed collection of real inputs with known-good answers, scored automatically on every change.
When you need it: Without one, nobody can say whether last week's change helped or hurt — which is how AI projects stall.
Explicit limits on what the model may say, do or call, enforced in code around the model rather than asked for in the prompt.
When you need it: When the model is customer-facing or able to trigger an action, a prompt is a request; a guardrail is a rule.
Capability Maturity — we walk you up the five levels
Operated at Level 4 · Quantitatively Managed — measured, predictable and owned. We track this rank and raise it over time. See the five levels →
Why this matters
Functional value, value-stream value — and why it can't be ignored.
Integrates large language models into existing products, workflows and data — retrieval over your own content, structured outputs, evaluation harnesses, guardrails, fallback and per-call cost control.
The judgement layer of the value stream: it puts reading, classifying, drafting and deciding where a person used to sit, so work that queued for attention now completes on arrival.
Most LLM projects fail in the same two places: the model answers from the wrong source, and nobody measured whether it was right. Both are engineering problems with known answers — and both are far cheaper to solve before launch than after a customer finds the error.
One data layer · measured & owned
Every service creates data — captured, measured, owned.
LLM Integration doesn’t just run — it generates signal. We land that data in one warehouse and turn it into live KPIs tied to a named owner, so performance is attributable and acted on, never trapped inside a single tool.
Data warehouse
Supabase
Snowflake
Keboola
BigQuery KPIs, dashboards & apps
Plotly
Retool
Apache Superset Platform-agnostic — we connect what you already use and add a warehouse only where it earns its place.
How we select technology
We choose technology last — for the outcomes it has to earn.
Never tech for its own sake. A tool joins the stack only where it measurably protects value, cuts cost or unlocks speed — fit to a designed system, owned where it counts, and measurable from day one. How we select technology →
Speed
Modern, edge-served stacks are sub-second. Slow surfaces cost conversion and SEO every single day.
Cost
Cut licence, hosting and per-task SaaS fees — pay for capability, not lock-in or idle enterprise tiers.
Security
Flat, edge architecture with almost nothing left to attack — fewer moving parts, smaller surface.
Ownership & no lock-in
Composable and self-hosted where it counts — you own the system, not rent someone else’s.
Measurability
Clean data and KPIs baked in — you cannot improve, automate or apply AI to what you cannot measure.
Fit to process
The right tool for a designed system — selected against process readiness, so it amplifies, not adds friction.
How we deliver · DMAIC
Every LLM Integration engagement runs on DMAIC.
Define the goal and its value, measure the baseline, analyse the real constraint, improve with a proven build, then control the gains — so results are predictable, repeatable and defensible, not down to luck.
Agree the goal, value, budget & timescale up front.
Baseline the KPIs above — current state, not guesswork.
Diagnose the real constraint and the solution needed.
Build the chosen solution; prove the uplift.
Lock in the gains; monitor and sustain them.
FAQ
Common questions
What is LLM Integration?
Access to a model is not a capability. We integrate large language models into the products and workflows you already run — grounded in your own data, constrained to a defined job, evaluated against real examples, and costed per transaction before anything goes live.
Why does LLM Integration matter?
Most LLM projects fail in the same two places: the model answers from the wrong source, and nobody measured whether it was right. Both are engineering problems with known answers — and both are far cheaper to solve before launch than after a customer finds the error.
How does Sentry deliver LLM Integration?
Integrates large language models into existing products, workflows and data — retrieval over your own content, structured outputs, evaluation harnesses, guardrails, fallback and per-call cost control.
What do I get with LLM Integration?
Grounded in your data: Retrieval over your own documents, records and systems, so an answer points at something real instead of being invented. Structured output, not prose: The model returns validated fields your software can act on — the difference between a demo and a working integration. Evaluated before it ships: A test set of real examples, scored on every change, so a new prompt or a new model cannot quietly make things worse. Costed and capped: Cost per call measured, work routed to the cheapest model that passes, with limits and fallbacks so spend cannot run away.
Start here · the free first step
LLM Integration starts the same way every engagement does: with Discovery.
Two distinct moves — a free, no-obligation Discovery Session to find your value at stake, then the Discovery & Blueprint: 14 structured outputs — value model, roadmap and business case — before a pound of delivery is committed.
Engaged alone or as one engine