Capability
Agentic AI Engineering
AI agents and production AI, engineered into the products and workflows you already run.
Tell us about your problem
Overview
AI features and agents only earn trust when they're engineered, not just prompted into existence. We build production AI systems, from AI-powered features to agents that carry out multi-step work, that work with your existing data, software, and business rules.
What We Build & What You Get
Capabilities and deliverables.
Technical Capabilities
AI-powered features built directly into your product
AI tools built on large language models (the kind of AI behind ChatGPT), added to your workflow
AI agents that can carry out several linked steps on their own, within limits you set
Pulling the right documents, records, and messages together from across your systems
Turning your policies into written rules, so suggestions stay consistent
Approval and escalation steps, where a person checks the AI's work, for cases that need it
Checks on AI quality, and alerts if quality slips over time
Adding AI to software that wasn't built with AI in mind
Deliverables
A working AI feature or workflow, built for a real product or operational need
A written Decision Rulebook the AI follows every time, where judgment calls are involved
An approval record a reviewer can trust: what was suggested, and who approved it
A quality score to start from, so quality is measured, not assumed
Where We Fit
Not the focus here: General software or product engineering with no AI component, see Software Engineering.
Not the focus here: The system integration work an AI feature depends on but doesn't itself build, see Data & Systems Engineering.
How We Engage
First a limited assessment, then a build. Where a decision needs approval before it acts, we start read-and-recommend and only widen autonomy after that's proven out in production.
Exception-to-Action Engineering
Where Agentic AI Engineering fits the method.
Every engagement follows the same five stages. This capability carries the stages highlighted below.
Map the Problem
Exception Map + Baseline Sheet
Bring the Data Together
Context Map + Decision Rulebook
Human Approval
Human Approval Flow
Measure the Result
Measurement Report
Go Live
Action Layer
Implementation Examples
Related implementation examples.
Material nonconformance and supplier quality exceptions
A representative scenario based on a recurring bottleneck in mid-size manufacturing: incoming material fails quality inspection, requiring manual investigation across systems.
Read the blueprint
Supplier delivery exceptions and purchase-order mismatches
A representative scenario based on a recurring bottleneck in mid-size manufacturing procurement: a purchase order, receipt, and invoice don't match, or a supplier delivery arrives late or partial, and someone has to work out what to do next.
Read the blueprint
Production schedule disruptions and machine-down exceptions
A representative scenario based on a recurring bottleneck on the plant floor: a machine goes down or a material shortfall disrupts the production schedule, and a planner has to work out the impact and re-sequence the schedule under time pressure.
Read the blueprint
Order exceptions: failed payments, oversold stock, and shipping failures
A representative scenario based on a recurring bottleneck in mid-size commerce operations: an order fails somewhere in the pipeline (a payment hold, an oversold SKU, a carrier exception), and someone has to triage it across systems before the customer notices.
Read the blueprint
Product data out of sync: pricing, inventory, and catalog mismatches
A representative scenario based on a recurring bottleneck in mid-size commerce operations: product data drifts out of sync between the PIM, the ERP, and what customers actually see, creating pricing errors, overselling, and lost sales before anyone notices.
Read the blueprint
Adding an AI feature without breaking what already works
A representative scenario based on a recurring bottleneck for SaaS product teams: shipping an AI-powered feature (smart search, auto-categorization, a support copilot) inside a product that wasn't built with AI failure modes in mind, without destabilizing what existing customers already depend on.
Read the blueprint
Reconciliation exceptions with a full audit trail
A representative scenario based on a recurring bottleneck in mid-market financial operations: a reconciliation run produces breaks that don't auto-match, and each one has to be investigated, resolved, and documented well enough to survive an audit.
Read the blueprintFrequently Asked
Next Step
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