Cognitive automation for processes where the exceptions need judgment.
When the happy path is mechanical but the exceptions need judgment, we bridge RPA with LLM reasoning — and audit every decision.
- Typical duration
- 10–16 weeks
- Squad
- 3–5 senior engineers
- Starting at
- $32,000 / month
- Automation rate lift
- +31 ptsAutomation rate lift
- Exception queue reduction
- −54%Exception queue reduction
- Decisions with full audit trail
- 100%Decisions with full audit trail
Your RPA estate automated the easy 60%. The rest needs judgment.
Classic RPA excels at deterministic, stable processes and collapses on variation. That's why most estates plateau: the remaining volume is exactly the work that requires reading context and making a call. Bridging existing bots with model-driven reasoning unlocks that tail without rebuilding what already works.
Sound familiar?
- An RPA program whose automation rate stopped climbing
- Bot maintenance costs approaching the savings they generate
- Exception queues growing faster than the automated path
- Compliance unable to explain why a bot did what it did
How we deliver intelligent process automation.
Four phases, each with a written definition of done. You will always know which phase we are in and what has to be true to leave it.
Audit the estate
We inventory existing bots, measure where they break, and quantify the exception volume that never gets automated.
Bridge
A reasoning layer that sits alongside your bots — invoked precisely when the deterministic path hits variation it can't handle.
Route exceptions
Confidence-tiered routing: high-confidence cases proceed, mid-confidence cases get a fast human check, low-confidence cases escalate.
Prove it
Decision audit trails, per-policy reporting, and a control framework your internal audit function can actually test.
What is included.
Every engagement is scoped to your problem, but these are the capabilities we bring to the table.
RPA + LLM bridging
Reasoning services callable from UiPath, Blue Prism, or Power Automate — so existing investment is extended rather than replaced.
Exception routing
Confidence-tiered handling that sends only genuinely ambiguous cases to people, with the evidence pre-assembled for them.
Decision audit trails
Immutable records of inputs, policy version, model version, and rationale for every automated decision — retained for your audit window.
Approval workflows
Configurable multi-tier approvals with delegation, SLA timers, and escalation paths that mirror your existing authority matrix.
Process mining
Event-log analysis that finds the true bottleneck rather than the one everyone assumes, before a line of automation is written.
Control framework
Documented controls mapped to SOX, SOC 2, or your internal framework — written so an auditor can test them without a walkthrough.
Technology we typically reach for.
Chosen per engagement against your constraints — never because it is the fashionable choice this quarter.
- Claude
- UiPath
- Power Automate
- Python
- Temporal
- Postgres
What this looks like in production.
Cutting prior-auth cycle time by 71%
Northwind Health
- Problem
- Manual prior authorization consumed 6+ hours per case and delayed care for thousands of patients.
- Solution
- We built a HIPAA-compliant agent that drafts letters, attaches evidence, and routes to payers via existing APIs.
- Outcome
- Average cycle time fell from 4.2 days to 1.2 days. Denials dropped 38% in the first quarter post-launch.
- Cycle time reduction
- 71%Cycle time reduction
- Denial reduction
- 38%Denial reduction
- Hours saved / clinician / week
- 14Hours saved / clinician / week
Capabilities that pair well with this one.
Tell us what you're trying to solve.
A 30-minute call with a senior engineer — no SDRs, no discovery deck. You will leave with an honest read on whether this is the right capability and what it would take.