LLM-integrated applications
Language features built into the product—drafting, summarizing, extraction, search—behind the same APIs and auth as everything else.
ServicesSoftware & AI engineering
AI Engineering
LLM-integrated applications, retrieval, and agent systems—shipped behind real APIs, with evaluation and guardrails that hold up in production.
What it means
We build AI into products rather than beside them. That means retrieval over your own content, tools the model can actually call, and the evaluation and fallback behaviour that decide whether any of it is safe to put in front of a user.
Answer questions over your own documents and data with retrieval that cites what it used.
Give the model real tools—your APIs, your systems—so it can do work, not just describe it.
Automate multi-step workflows with agents that have defined boundaries and a stopping condition.
Catch regressions before users do, with evaluation you can rerun on every change.
Keep costs and latency predictable through caching, routing, and model choice.
Fall back safely when confidence is low, with a human path for the calls the model shouldn't make.
What it covers
Retrieval and RAG
Chunking, embeddings, and vector search over your own content, with citations so an answer can be checked rather than trusted.
Agent systems
Multi-step workflows built with LangChain and LangGraph, with explicit tool boundaries, retries, and a defined stopping condition.
Guardrails and fallbacks
Input and output filtering, confidence thresholds, and a human path for the decisions a model shouldn't be making alone.
Evaluation harnesses
Repeatable test sets so you can tell whether a prompt, model, or retrieval change actually made things better.
Cost and latency control
Caching, model routing, and streaming, so the feature stays affordable and fast as usage grows.
How we work
Find the real use case
We start with a task someone does today and what it costs to get wrong. A lot of AI ideas are better served by a query or a rule, and we'd rather say so early.
Check the ground truth
Retrieval is only as good as what it retrieves. We look at the content you'd be answering from and whether it's accurate, current, and complete enough to build on.
Build a thin version
One narrow flow, end to end, with evaluation attached—so the question becomes whether it works rather than whether it could.
Evaluate honestly
A fixed test set, run on every change, including the awkward inputs. Vibes-based prompt tuning is how teams ship regressions without noticing.
Ship with guardrails
Filtering, thresholds, fallback behaviour, and logging that lets you reconstruct what the model saw and why it answered as it did.
Questions we hear often
Do you train your own models?
No—we build on hosted and open models rather than training from scratch, because for almost every product problem that's the faster and cheaper route to something that works. If a problem genuinely needs a custom trained model, we'll tell you that rather than take the work.
How do you stop it making things up?
Mostly by not asking it to. Retrieval grounds answers in your own content with citations, guardrails filter what goes in and out, and confidence thresholds route the uncertain cases to a person. You reduce hallucination structurally, not by asking the prompt nicely.
How do we know a change made it better?
An evaluation harness—a fixed set of inputs with expected behaviour, run on every prompt, model, or retrieval change. Without one, teams tune prompts by feel and ship regressions they don't notice for weeks.
What will it cost to run?
It depends on model choice, context size, and traffic, and it's very easy to design something that works in a demo and is unaffordable in production. We size that early and use caching, routing, and smaller models where they're good enough.
Will this plug into our stack?
Yes—by design. We prefer boring API contracts, event streams, and the identity your platform team already operates. Custom glue is documented the same way product features are.
Adjacent services
Ready to turn an AI brief into something shippable?
Send context—constraints, timelines, and what “good” means on your side. We'll reply with a grounded next conversation about what's actually involved.