We design, build, and deploy production-grade AI agents for regulated workflows — agents that read documents, make decisions, route work, and escalate to humans exactly when they should. XAI on every output. HITL on every decision that matters.
Teams spend hours reading, extracting, and routing documents that an AI agent could handle in seconds. Manual processing introduces inconsistency, creates backlogs, and prevents underwriters and analysts from spending time on decisions that actually need them.
Most agent projects fail not because the underlying technology doesn't work, but because the agent was never designed for the reality of production — variable inputs, edge cases, regulatory constraints, and the need for explainable, auditable outputs.
Regulators require that AI-assisted decisions be explainable and auditable. Agents built without explainability baked in create compliance exposure the moment they touch a customer-facing or regulatory-sensitive output.
HITL is mandatory in regulated environments — but it doesn't have to create bottlenecks. Poorly designed HITL workflows negate the speed benefits of automation. Well-designed ones free experts to focus on the decisions that genuinely need them.
The most sophisticated agent is worthless if it can't write to your policy admin system or read from your CRM. Integration complexity — legacy APIs, ACORD standards, proprietary formats — is where most automation projects stall after the demo.
Without defined accuracy thresholds, confidence scoring, and ongoing performance monitoring, automated systems degrade silently. Production AI needs the same quality controls as any production software system.
Strategy engagements that are fixed-scope, time-boxed, and leave you with something you can act on immediately.
Every automation engagement starts with the same question: what decision or task are we automating, and what does it mean to get it wrong in a regulated environment? That question shapes everything — the agent architecture, the HITL design, the XAI requirements, and the integration path.
Every production-grade agent starts with a design specification that defines task boundaries, tool permissions, escalation logic, output schema, and failure modes — before any code is written. This is the most important step, and the one most projects skip.
We define accuracy thresholds, confidence scoring, and validation test cases at design time — then build the evaluation framework alongside the agent itself. Every agent is tested against a hold-out validation set of real documents before it touches production data.
Explainability is not a feature we add at the end — it's an architectural requirement that shapes every agent we build. Every output carries a structured explanation that traces the reasoning back to source documents, which satisfies regulators, builds user trust, and makes debugging reliable.
Human-in-the-loop is mandatory in regulated AI — but it doesn't have to create bottlenecks. We design HITL interfaces that surface exactly the information reviewers need, route only the outputs that genuinely require human judgement, and capture every override for model improvement.
Agents are only valuable when they connect to the platforms people work in daily. We build the API layers, webhook handlers, and integration connectors that embed agents into existing policy admin systems, CRMs, and case management tools — without requiring platform replacements.
Our agent and automation work runs on the leading AI and cloud platforms. Every technology recommendation is made independently — no vendor commissions, ever.
Explore our technology partners
Readiness assessments, roadmap design, and operating model advisory before you commit to build.
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AI-ready data foundations on Microsoft Fabric, Databricks, and Snowflake — no vendor lock-in.
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Compliance reviews and ROI frameworks that justify AI investment and satisfy regulators.
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30 minutes. Tell us the workflow you want to automate. We'll tell you honestly whether an agent is the right approach, what it would take to build it right, and what it would cost.