DNR Labs designs, builds and operates custom agentic systems inside the tools your team already uses. We start with the workflow that eats your week, not with a platform migration.
How we work
Every engagement is scoped to a single operating metric and a delivery date. Cycle time on a queue. Cost per ticket. Hours a team spends re-keying data. If we cannot name the number in the first conversation, we will tell you that instead of selling you a pilot.
Agents sit on top of the CRM, ERP, ticketing system and document store you already pay for. No rip and replace, no data migration project, no third platform for your team to learn. If a workflow spans five systems, the agent spans five systems.
Code, prompts, evaluation suites, runbooks and dashboards are handed over and documented. There is no black box, no per-seat licence on your own process, and no version of this where you cannot fire us and keep working.
What we build
Reps spend a third of their week on research and data entry. We move that to agents and leave the judgment calls where they belong.
Where it usually lands first: reps get back the hours they were spending inside the CRM.
Chat and voice agents grounded in approved sources, with a visible citation on every answer and a clean handoff the moment confidence drops.
Where it usually lands first: the repetitive half of the queue stops reaching a person at all.
Operations breaks at the seams between systems. Agents are good at seams, and they never get tired of checking.
Where it usually lands first: the exception desk stops being somebody's entire job.
Back office work is high volume, rule-bound and audited. That is close to the ideal shape for an agent, provided the audit trail is real.
Where it usually lands first: close gets shorter and the audit trail gets better at the same time.
Most engineering teams are not short of ideas. They are short of hours and stuck behind code nobody wants to open.
Where it usually lands first: the backlog of small internal requests finally clears.
In regulated work the constraint is rarely knowledge. It is how many documents a qualified person can read in a day.
Where it usually lands first: review stops being the bottleneck on everything downstream.
Industries
A queue of work, rules that mostly hold, exceptions that need judgment, and a system of record that has to stay clean. We have built against that shape in each of these.
Document intake, exception handling, carrier and vendor chasing, status visibility across systems that do not talk.
Claims intake and adjudication support, prior authorisation, coding checks, member and provider correspondence.
Dispatch and scheduling, job report capture from the field, parts and warranty checks, first-line technician support.
Clause extraction, playbook deviation review, obligation tracking, continuous control monitoring with citations.
Support deflection, onboarding and migration work, internal tooling, and engineering throughput on legacy code.
KYC and onboarding review, reconciliation, dispute handling, and reporting packs assembled before anyone asks.
The method does not change. We map your workflow, find the steps that follow rules, and put agents on those. Manufacturing, education, real estate, media, public sector: if the work runs on a process, it is in scope.
Engagement
We sit with the team doing the work and instrument what actually happens, which is never what the process document says.
Agents are built against your real data and your real edge cases, with the evaluation suite written before the agent is.
Live traffic, with your team on the dashboard. We stay on until the number holds without us, then we hand over the keys.
For complex environments we place a forward deployed engineer with your team for the length of the build. They sit in your standups, learn your systems, and ship inside your constraints rather than emailing specifications back and forth. Most engagements do not need it and we will say so. When the workflow spans several teams and a decade of accumulated exceptions, it is the difference between a demo and a deployment.
Platform
An agent on its own is a demo. What makes it survive contact with production is everything around it: the context it reads, the evaluations it is scored against, and the people who catch what it gets wrong.
We write the test set before the agent. If we cannot measure it against your real cases, we do not put it in front of your customers.
Agents handle the pass. People handle the exceptions, and every decision they make becomes a training example for the next run.
Everything is versioned, documented and deployable by your team. The handover is part of the engagement, not an upsell.
Start here
You do not need a brief, a budget or an AI strategy. One paragraph about the thing your team keeps doing by hand is enough for us to know whether we can help.