Find the expensive task
We start with the workflow that consumes the most hours or leaks the most revenue — not with the model. If automation doesn't pay for itself, we say so before you spend.
Applied AI
AI Agents & Automation
Agents that do the work, not demos that describe it.
What this covers
- Task agents that run real workflows — triage, routing, follow-up, escalation
- RAG knowledge assistants grounded in your own documents and systems
- Document extraction: invoices, contracts, forms, resumes, statements
- Classification and enrichment pipelines with confidence thresholds
- Human-in-the-loop review queues for anything consequential
- Evaluation harnesses so accuracy is measured, not assumed
- Self-hosted and in-region deployment where data cannot leave your infrastructure
What you walk away with
- Running agents integrated into your existing tools
- Admin controls: thresholds, overrides, audit history
- Documented prompts, evaluation results and failure modes
- Full source, deployed in your infrastructure or ours
How we work
Prove it on real data
A narrow prototype against your actual documents and edge cases, scored against a held-out set. You see accuracy before scope, not after.
Ship with guardrails
Confidence thresholds, fallback paths, and a review queue for low-confidence outputs. The agent escalates rather than guesses.
Measure and tighten
Logged runs, accuracy tracking, and a feedback loop that improves prompts and retrieval over time.
Frequently asked
Which models do you build on?
Whichever fits the task and the budget — we're model-agnostic and design so the provider can be swapped without a rewrite. Selection is driven by accuracy on your data, latency, and cost per run.
Will our data be used to train someone else's model?
No. We use enterprise API tiers with training disabled, and for sensitive workloads we architect around self-hosted or in-region models instead. Data handling is documented before we start. If processing location is a hard requirement rather than a preference, see our AI Workforce page — that is the same engineering under a stricter constraint.
What if the AI gets something wrong?
That's designed for from day one. Confidence thresholds route uncertain outputs to a human review queue, every run is logged, and we agree accuracy targets before shipping rather than hoping for the best.
Scope an AI agent.
Tell us what you're trying to solve — we'll scope it honestly, including whether it's worth doing.