Design
We map the workflow, the data, the rules, and the decision points — then define exactly where an agent creates leverage and where a human must sign off.
Meridian designs, builds, and operates AI agents for the document- and rules-heavy work behind enterprise operations — engineered with the governance, testing, and auditability that regulated industries demand.
01 / What we do
The shape of every engagement — design, build, operate.
We start from a real workflow — the estimate that takes three days, the rebate file no one wants to assemble — and build an agent that does it end to end, with your people in the loop where judgment matters.
We map the workflow, the data, the rules, and the decision points — then define exactly where an agent creates leverage and where a human must sign off.
We construct the agent, its tools, and its harness on our platform — connected to your systems, evaluated against real cases, and gated behind approvals.
We run it in production with monitoring, evaluations, and on-call support — then hand your team the controls to own it as it scales across the organization.
02 / The platform
The layers that make an agent safe to run in production.
The difference between a demo and a deployment is everything around the model. Meridian is the harness, the evaluation suite, the deployment pipeline, and the control plane — so every agent is tested, governed, and accountable.
A typed, permissioned runtime that gives agents controlled access to your systems — every tool call scoped, rate-limited, and recorded. No agent touches production data without an explicit, revocable grant.
Golden datasets, regression suites, and adversarial red-teaming run on every change. An agent ships only when it clears accuracy, safety, and cost thresholds — and we watch those metrics in production, not just at launch.
Versioned prompts, tools, and policies move through staging with canary rollouts and one-click rollback. Every release is reproducible and attributable to a person and a change.
Immutable logs, human approval gates, PII controls, and role-based access — mapped to SOC 2, GDPR, and the emerging standards for agentic AI. Show any decision, from input to output, on demand.
Connectors to the systems your work already lives in, with signed data-processing terms, defined SLAs, and clear ownership of models, data, and outputs.
Multi-seat workspaces, roles, and audit trails so your team can review, approve, and eventually own the agents — with onboarding built for operators, not just engineers.
03 / Case study
We worked with a regional commercial energy-efficiency contractor — a long-standing utility Direct Install partner — whose assessment-to-completion pipeline was exactly the kind of high-volume, rules-bound work this platform is built for.
“From the original analysis to contracts to project management to completion — we handle it all. We want that same rigor, at ten times the throughput.”
— Operations lead, energy-efficiency contractor
Utility bills, site surveys, and equipment inventories become a structured energy profile in minutes.
Direct Install and PSE&G program rules applied automatically to size incentives of up to 80%.
The utility paperwork no one wants — assembled, checked, and submission-ready.
Our conviction
The companies that win the next decade won't be the ones that talk to AI. They'll be the ones whose work runs on it — safely.
Start the conversation
Tell us about a workflow. We'll show you what a governed agent could do with it — and exactly how we'd get it to production.