Build AI. Automate work. Trust the outcome.
Six disciplines that move an enterprise from AI experimentation to production — agents that do real work, evaluated before they ship, governed once they're live. Pick the one you need, or bring us a workflow that spans all six.
AI Agent Development & Automation
We build enterprise AI agents that do a specific job end to end — retrieve the right information, call the tools they're authorized to use, execute a workflow, and escalate to a human exactly where the rules say they should. Not a chatbot demo — a system that finishes the task.
- HR, finance, IT support, customer service, procurement, and document-processing agents
- Tool use and system integrations scoped to what the agent is actually authorized to touch
- Human escalation built in at the decision points that call for it, not bolted on after launch
AI Agent Quality & Evaluation
Before an agent goes near a real customer or a real decision, we test it the way its failure modes actually show up: accuracy, hallucination rate, task completion, tool-use correctness, prompt-injection resistance, data leakage, policy compliance, cost, and latency — scored, not asserted.
- A test suite and evaluation scorecard built around your agent's actual failure modes
- A named Production Readiness status, not a vague 'looks good to us'
- One-time assessments or recurring evaluation as the agent and its usage evolve
AI Governance & Risk
Once AI is in production, someone has to be able to answer who owns it, what it can access, and what happens when it's wrong. We build the AI/agent inventory, permissions review, oversight policy, and audit trail that makes an enterprise AI deployment defensible, not just functional.
- A real AI/agent inventory — what's running, who owns it, what data it can touch
- Human-oversight policy, incident response, and lifecycle controls, not a policy PDF nobody follows
- Built as an independent check, not graded by the same team that built the agent
AI Workflow Automation
We automate the specific process that's currently eating the most manual hours — invoice validation and approval, email triage into your CRM, document review and routing — combining AI judgment with your existing systems and a human approval step, then measure it before asking you to trust it with more.
- One workflow automated end to end first, with integrations into the systems you already run
- A human approval path built into the flow, not removed for the sake of a demo
- A measurement dashboard showing exactly what the automation is and isn't handling
AI Data & Knowledge Systems
An AI agent is only as good as what it can retrieve. We build the ingestion, retrieval (RAG), and permission-aware knowledge layer underneath — so answers are grounded in your real documents and data, respect who's allowed to see what, and cite where they came from.
- Enterprise document ingestion and permission-aware retrieval, not a flat unsearchable dump
- Source-grounded responses your team can actually verify, not confident-sounding guesses
- Built to support the agents and automations above, not a standalone science project
AI Decision Systems
The forecasting and dashboard work Quintessence Analytics already does well doesn't disappear — it becomes one more input into the same decision architecture: monitor the real signal, analyze it, predict what's coming, recommend the move, alert when something changes, and act — with a senior analyst reviewing every AI-generated output before it reaches you.
- Driver-level forecasts with the assumptions attached, not a single unexplained number
- Interactive, filterable dashboards built to be queried, not a static export
- Every AI output reviewed by a senior analyst before it ever reaches a client