How do you handle hallucination?

Architecturally, not by prompting. Grounding verification checks that each generated claim traces to retrieved content, and ungrounded claims trigger escalation rather than delivery. The measurable goal is a system that reliably knows what it doesn't know.

Which models do you work with?

We're model-agnostic and generally recommend routing across several by task. Model choice changes faster than architecture, so we build the evaluation and retrieval layers to make switching a configuration change.

All services

Service

Applied AI

Production LLM systems, evaluation, and MLOps.

We take AI from demo to dependable system: retrieval architecture, evaluation harnesses, guardrails, cost control, and the monitoring that catches drift before your customers do.

Discuss an engagement

Capabilities

What this includes

Evaluation infrastructure

Versioned eval sets and CI gates so you can tell whether a change made quality better or worse.

Retrieval architecture

Hybrid retrieval grounded in your corpus, with claim-level source tracing.

Guardrails & escalation

Systems that recognize what they can't answer and route it to a human cleanly.

Cost engineering

Routing, caching, and model selection tuned to cost per resolved task rather than per token.

Outcomes

What you get

Objective quality measurement before and after every change
Grounded answers with traceable sources
Predictable inference cost as volume grows

Engagement models

How we structure it

AI readiness assessment

Three weeks. Corpus audit, use-case triage, and a candid view of what is and isn't feasible.

Pilot to production

Take one high-value use case from prototype to a system with evaluation, guardrails, and monitoring.

Platform build

The shared infrastructure — evaluation, retrieval, observability — that subsequent use cases build on.

Questions

Things clients ask us

Usually because there's no way to tell whether it's getting better or worse. Demos are evaluated subjectively on a handful of examples; production needs a versioned eval set and a regression gate. That's typically the first thing we build, before touching the model.

Related work