Automate the workflows that scale linearly with headcount so your team stops doing them.
Replace brittle scripts and manual handoffs with intelligent workflows that read, decide, and write into your systems of record.
- Typical duration
- 8–12 weeks
- Squad
- 3–4 senior engineers, 1 designer
- Starting at
- $32,000 / month
- Manual touches removed
- 78%Manual touches removed
- Cycle time reduction
- 71%Cycle time reduction
- Straight-through processing
- 62%Straight-through processing
The work that scales with headcount is the work AI should absorb.
Every growing operation accumulates workflows where a human reads a document, makes a routine judgment, and types the result into another system. RPA breaks the moment a form changes. Pure LLM automation is unpredictable. The answer is a hybrid — deterministic where it can be, model-driven where it must be, and audited throughout.
Sound familiar?
- Ops headcount rising in lockstep with transaction volume
- RPA bots that break every time a vendor changes a PDF layout
- Backlogs measured in days for work that takes minutes
- No audit trail explaining why a decision was made
How we deliver ai 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.
Map
We shadow the actual process — not the documented one — and time every step. The map shows where volume concentrates and where judgment is genuinely required.
Split
Deterministic steps get code. Judgment steps get models. Ambiguous steps get a human checkpoint. The split is explicit and reviewable.
Build
Extraction pipelines, orchestration, and the review UX your operators will live in daily — designed with them, not for them.
Harden
Exception routing, confidence thresholds, replayable traces, and the dashboards that prove the system is behaving before you scale volume onto it.
What is included.
Every engagement is scoped to your problem, but these are the capabilities we bring to the table.
Document processing
Extraction from PDFs, scans, emails, and forms with confidence scores per field — so low-certainty extractions route to a human instead of downstream.
Workflow orchestration
Durable, resumable workflows that survive restarts, retry intelligently, and never double-write into your system of record.
Human-in-the-loop review
Review queues designed for throughput: keyboard-first, side-by-side source evidence, and one-keystroke approve or correct.
Exception handling
Explicit policies for what happens when the model is unsure, the source is malformed, or a downstream system rejects the write.
Systems integration
Clean writes into Salesforce, NetSuite, SAP, ServiceNow, or whatever else runs your business — idempotent and reconciled.
Audit & reporting
Every automated decision recorded with its inputs, model version, and confidence — queryable for the quarter-end review.
Technology we typically reach for.
Chosen per engagement against your constraints — never because it is the fashionable choice this quarter.
- Claude
- OpenAI
- LangGraph
- Temporal
- Python
- 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.