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What we build
Autonomous agents that execute real work — triaging tickets, reconciling documents, operating internal tools — and copilots that sit inside the software your team already uses. Under them: retrieval-augmented generation (RAG) pipelines over your own data, so answers come from your knowledge, not the model’s imagination.
For workflows too big for one agent, we design multi-agent orchestrations: planners, workers and reviewers with explicit hand-off points, checkpoints a human can approve, and full audit trails of every decision and tool call.
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What makes it production-grade
Evaluation harnesses, before anything else: a scored test set that tells you — with numbers — whether this week’s system is better than last week’s. Guardrails on inputs, outputs and tool access. Observability on every step, so when an agent behaves oddly you read a trace instead of guessing.
We are model-pragmatic: Claude and OpenAI models, orchestrated with LangGraph and paired with vector databases, chosen per task by measured quality and cost — never by hype.
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Where to start
The best first agent is a high-volume, low-glamour workflow with a clear definition of done — support triage, document intake, data hygiene. Prove reliability there, earn the organisation’s trust, then expand. We help you pick that first workflow in the Decrypt call.
05 / Decrypted questions
Asked before every mission.
What is agentic AI, in plain terms?
Agentic AI is software that plans, calls tools and completes multi-step work autonomously — not just answering questions, but doing tasks: looking things up, updating systems, escalating to a human when confidence drops.
How do you stop an agent from hallucinating?
Three layers: ground answers in your data with RAG so the model cites instead of invents; constrain actions with schemas and guardrails; and measure everything with an evaluation set so regressions are caught before users see them.
Do we need our own model or fine-tuning?
Rarely. Most production value comes from retrieval, orchestration and evaluation on top of frontier models like Claude. Fine-tuning enters only when measurements show a gap that prompting and retrieval cannot close.
How long until an agent is in production?
A scoped first agent typically reaches supervised production in 6–10 weeks: two weeks of Decrypt and data groundwork, then weekly increments with the evaluation harness running from week one.
What does an agent cost to run?
Token costs are engineering choices: routing simple steps to cheaper models, caching aggressively and keeping context lean typically lands per-task costs at cents, not dollars. We report cost per completed task alongside quality metrics.