AI in your operations
We take a process that lives in people's heads and inboxes, give it a shape, and let AI take the steps that repeat: reading, sorting, drafting, checking, routing. Your team keeps the decisions and runs it without a developer.
Signs it's time
The order that works
Packages
We sit with the people who run the process and write it down as it really happens: steps, handoffs, the tools each step touches, the exceptions, and the decisions that need a person.
The predictable steps run on their own: reading, sorting, drafting, checking, routing. People get the decisions, with everything they need on one screen, and approve anything that cannot be undone.
Your CRM, helpdesk, inbox, calendar, Slack or database, connected to an AI model or assistant through APIs, webhooks and MCP. One integration done properly, with limits and logs.
Your team already pays for ChatGPT or Claude, and everyone still starts each task from a blank prompt. We turn your playbooks, templates and house style into skills the assistant follows, and connect it to the tools your team works in.
For an AI pilot that impressed in the demo and stalled in real work. Traces on every run, a test set built from your real failures, a check before each release, and a person in the loop where a mistake costs money.
Proactively identifies opportunities to streamline workflows and create long-term efficiency.
Denys consistently showcased his expertise and strong understanding of business requirements.
AI model costs are billed at cost, without a markup, and estimated per run in the map.
AI quality: audits and observability
Where your agent breaks once real users touch it. We read your traces and code, score the setup against a 17-point readiness checklist, cluster the failures, check cost per conversation and what an injected prompt could make it do, and rank the fixes.
Something broke in production and nobody knows why. We read the traces, isolate the root cause, record a walkthrough, and send the fix as a pull request or exact steps.
Traces on every step, not only the model call: tools, retrievers, routing. Environments kept apart, evaluators for what good means in your domain, a test set from real failures, dashboards and alerts.
The people who spot a bad answer (a support lead, a lawyer, a clinician) review it in an annotation queue, adjust the prompt with versions and rollback, run the test set, and approve. No sprint, no developer.
Every result traces back to the exact client statement it came from.
Unwavering commitment to getting to the root of any problem.
Background: AI incident tooling at BigPanda, where noisy signals from many systems had to become one decision for an on-call team. In the Structed AI interview: LangSmith traces with metadata on every run, prompts versioned in LangSmith Hub and changed without a redeploy, golden and regression sets per stage, annotation queues, and cost estimated per interview.
A Routine Run routine
Six of the seventeen points, the ones that fail quietly. Run them on your phone before customers meet your agent.
Keys out of the code
Secrets in a vault, not in prompts or the repo.
Production apart from staging
Separate projects, so a test never mixes with real traces.
Trace every step
Tools and retrievers too, not only the model call.
A golden set of 30 real cases
Run it before every prompt or model change.
Cost per run, with a limit
Know it per conversation, not from the monthly bill.
A runbook for the night it breaks
Who is alerted, what they check first, how to roll back.
Proof
Implementing our CMS, which empowered technical writers to update in-app integration docs independently, saving engineers countless hours.
Questions
They stop copying, sorting and chasing, and spend that time on the decisions and the clients. The judgement stays with them, and the map shows exactly which steps change for whom.
Often you should, and the map will say so. Tools automate one step; the trouble usually sits in the exceptions between steps and the decisions nobody wrote down. That part needs design first.
Anything with an API or webhooks: CRMs, helpdesks, email and calendars, Slack and Teams, databases, PagerDuty, Stripe, and AI assistants through MCP. If a tool has no API, we tell you what that costs.
Yes. The audit takes a week, works from your traces and code, and ends with a ranked list of fixes, each with a price. If you have no traces yet, setting them up is the first fix.
It will, sometimes. So every run is recorded with its reason, anything irreversible waits for a person, and each mistake someone reports becomes a test that runs before the next change.
