Enterprise AI,
strategy to governance

Enterprise AI consulting services include AI strategy, custom AI systems, LLM integration, and governance, all within one accountable operating model.

All services →
01 / SERVICE
Strategy
AI readiness, use-case prioritization, and executive roadmaps.
→
02 / SERVICE
Systems
Custom AI systems: LLM applications, AI agents, and RAG.
→
03 / SERVICE
Integration
Legacy integration, API orchestration, and workflow automation.
→
04 / SERVICE
Governance
Security review, model monitoring, and compliance operations.
→

Production AI,
end to end

Products, solutions, and services run on one governed platform grounded in your data and ready for audit.

All products →
01 / PRODUCT
AI Assistant
Answers from your systems, actions taken, every response logged.
→
02 / PRODUCT
Service Management
Resolve more tickets automatically across IT and employee service.
→
03 / PRODUCT
Enterprise Search
One permission-aware answer across every system you run.
→
04 / PRODUCT
Agent Studio
Build and govern agents that take real action, within limits.
→

AI solutions,
by team and outcome

Explore enterprise AI solutions by team, outcome, and industry, including ITSM automation, self-service, cost reduction, and faster onboarding.

All solutions →
01 / INDUSTRY
Financial Services · Healthcare · More
Regulated, high-stakes sectors with one concrete use case each.
→
02 / INDUSTRY
Government
Secure, auditable public-sector AI.
→
EN हि ES FR AR
SOC 2 · ISO 27001 · Data residency on request
CS.01 Work: Financial Services

The claims queue, governed.

A national insurer replaced its manual claims review queue with a governed AI agent. Every decision is logged, and exceptions are escalated to a human adjudicator.

Sector
FINANCIAL SERVICES
System
AI AGENT + RAG
Timeline
11 WEEKS TO PRODUCTION
01 / CHALLENGE

A queue measured in weeks.

Claims arrived faster than adjudicators could review them. Most routine claims took as much senior attention as contested cases, increasing the backlog, review time, and cost as volume grew.

A previous automation pilot had stalled in security review: no audit trail, no escalation design, no answer to "who is accountable for a denial."

02 / APPROACH

Bounded autonomy, by design.

We started from the accountability question, not the model. The agent was given explicit authority boundaries: it can approve routine claims within policy terms; it can draft determinations; it cannot deny. Every denial path routes to a human adjudicator with the agent's full reasoning attached.

Policy documents were indexed into a permission-aware retrieval layer so every determination cites the exact clause it relies on.

03 / ARCHITECTURE

Four components, one audit trail.

Intake parses and classifies the claim. The policy RAG layer retrieves the controlling terms. The agent drafts the determination with citations. The review console routes exceptions and records the human decision. All four write to a single immutable log.

FIG. 01: AGENT TOPOLOGY
04 / OUTCOME

Faster, and defensible.

Within one quarter of cutover, routine claims cleared in hours instead of days, contested claims reached senior adjudicators sooner, and audit had a complete decision trail for the first time.

62%
reduction in average handling time
0
unreviewed denials, by construction
94%
of routine claims resolved within SLA
100%
of determinations carry policy citations

Have a queue like this?

Start a conversation →