LLM features that survive contact with production
Calling a model is a demo. Shipping it into a business process — with an evaluation harness, confidence routing, audit trails and a metric it answers to — is engineering. This page covers that machinery; what we build with it is our AI solutions service.
Why we reach for it
LLMs earn a place in the stack for engineering reasons — and stay there only while the numbers hold.
Unstructured work becomes automatable
Documents, emails and free text resisted automation for decades because they needed judgment. LLMs read them well enough to automate the bulk and route the rest to people.
Production machinery, not demos
Every integration ships with an evaluation harness built from your real cases, confidence routing that sends uncertain outputs to a human queue and audit trails on every decision.
Model-agnostic by design
The provider sits behind an abstraction with the harness on top. When a better or cheaper model appears, switching is a re-run of the test set — not a rewrite.
A metric attached, always
Every LLM feature we ship answers to a business number — hours saved, error rate, cycle time. If no metric can be attached, we advise against building it.
When we advise against it
An LLM is a tool, not a strategy. Three cases where reaching for one is the expensive way to be wrong.
The logic is deterministic
If a rule can be written down — pricing tiers, validation, routing by field values — a plain function is faster, cheaper, testable and never wrong in a new way. Most business automation still looks like this.
A smaller model beats an LLM
High-volume classification and forecasting on historical data are often better served by a purpose-built ML model — a fraction of the cost per prediction at equal or better accuracy.
A chatbot with no metric
If the brief is "we need AI" with no number attached, that's a pilot that will die between the demo and the P&L. We've written up why — and what to do instead.
What we build with it
The shapes LLM work most often takes in production — each covered in depth on its own page.
Document intelligence →
Contracts, invoices and claims read, extracted and routed — with confidence thresholds deciding what still needs human eyes.
Copilots for your team →
Sales, support and operations assistants grounded in your own data — with citations, permissions and audit trails built in.
Agentic workflows →
Multi-step processes where the model plans and executes under guardrails — every action logged, bounded and reviewable.
Owned AI infrastructure →
Open-weight models on hardware you control, for workloads and data that shouldn't leave — see how one company migrated off API bills.
Examples, not a boundary — if the workflow involves language, documents or judgment, it's probably in range.
How it fits the process
The free diagnosis decides whether an LLM belongs in your process at all — roughly a third of the time, our advice is not to build. When it does belong, the model choice follows the constraints — data sensitivity, volume, latency, budget — and LLMs sit alongside the rest of our stack as one option among several: defaults, not dogma.
Common questions
Which models do you work with?
The major commercial APIs and open-weight models, chosen per workload during the diagnosis. Because every integration is model-agnostic, the choice isn't a commitment — the evaluation harness turns switching into a measured decision.
How do you handle hallucinations?
By measuring instead of hoping. The evaluation harness scores accuracy against real cases before launch, confidence routing sends low-certainty outputs to a human queue and audit trails make every output traceable. The error rate becomes a known, bounded number.
Can this run on our own infrastructure?
Yes. When data can't leave your environment, we deploy open-weight models on infrastructure you control — same evaluation machinery, no third-party API in the loop.
Do we need clean data or an ML team first?
No. LLM integrations start from messy reality — the evaluation set is built from a few hundred real cases pulled from your operation, and the pipelines are built around whatever your data looks like today.
Have a process an LLM might fix — or a pilot that stalled?
The diagnosis is free, and if the honest answer is a plain function or no build at all, you'll get it in writing.