Primary outcome
extract meaning from documents at scale
What we ship
document intake pipeline, extraction schema, validation workflow, and exception queue
KPIs we report on
documents per hour, extraction accuracy, exception rate, and processing cost
What "automating document processing with AI" actually means
Automating document processing with AI is not a single product you buy. It is a workflow you redesign around AI as the operating layer. The agent handles the high-volume, high-structure tasks. Humans handle edge cases, exceptions, and trust-sensitive decisions. The system is instrumented to measure documents per hour, extraction accuracy, exception rate, and processing cost and improve weekly.
What changes by industry is the systems the agent integrates with, the data it retrieves over, the controls it operates under, and the KPIs it has to defend. The architecture is similar; the integration and the controls are different.
The architecture we use for AI document processing
- Frontier LLM — Claude, GPT-4-class, or Gemini. We benchmark candidates on a labelled test set during Discovery.
- Retrieval layer over your approved internal sources, with source citations on every output.
- Tool use for reads and writes against your operational stack (CRM, ERP, ticketing, data warehouse).
- Reviewer queue for low-confidence outputs. Confidence thresholds set per workflow.
- Evaluation harness — labelled test set, weekly accuracy reports, regression alerts.
- Versioned prompts and reviewer-action audit logs for traceability.
15 industries with a scoped engagement page for document processing. Each is a dedicated build with industry-specific systems, controls, and pricing.
How do you automate document processing with AI?+
We map your existing document processing workflow, identify high-volume and high-structure tasks, build an AI agent that handles those tasks, and route low-confidence cases to a human reviewer. The build connects to the systems your industry already runs on, runs against a labelled test set, and ships behind a reviewer queue before it sees production traffic. We measure documents per hour, extraction accuracy, exception rate, and processing cost from day one and improve weekly.
What is the best AI agent for document processing?+
There is no single off-the-shelf "best" agent for document processing — the right architecture depends on the systems and data of your industry. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for your stack, and a reviewer queue. We benchmark candidates against a labelled test set during Discovery and pick the model with the best accuracy/cost ratio.
What does AI document processing cost?+
Three phases, billed separately. Discovery sprint: $6k. Build engagement: $20k–$28k. Run retainer: $2.5k–$4k / mo. ~$32k–$58k typical year 1 (60% take the run option for ~6 months). Pricing varies slightly by industry — see the industry-specific pages below.
How long does it take to deploy AI document processing?+
Thin-slice in production in ~6 weeks after Discovery, full Build phase over 6-10 weeks. By day 90, documents per hour, extraction accuracy, exception rate, and processing cost is instrumented and you have a baseline against which to expand to adjacent workflows.
Which industries do you build AI document processing for?+
15 industries listed below have a scoped engagement page for document processing, each with industry-specific systems, controls, and KPIs. Common starting industries include Healthcare Providers, Pharmaceuticals, Medical Devices, Biotechnology, Retail, and others. Don't see yours? We build for any sector — tell us about your workflow and we'll scope it.
What do we own, and what do you own?+
We own workflow design, prompts, retrieval architecture, evaluation harness, and weekly improvement. You own data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
Selected portfolio
Real builds tied to document processing
A rotating selection of engagements where document processing was a primary driver, drawn from our active portfolio. Sectors and scope are accurate; client identities are withheld under engagement NDAs.
Q4 2025 → Q1 2026
Owners-association management SaaS — 55+ screens, 47 normalized tables
Mid-market property operator · GCC region
Full operational backbone for a property operator running multiple owners associations: properties, units, owners, accounting, service charges, budgets, maintenance, violations, and a resident-facing community portal — replacing a patchwork of spreadsheets and disconnected accounting tools.
- Next.js + tRPC
- PostgreSQL · Drizzle ORM
- JWT federated identity
Q4 2025
Internal automation tool — workflow automation for consulting operations
Multi-vertical consulting group · Europe
Internal automation tool to streamline workflows, reduce manual administrative load, and improve operational efficiency across consulting and management processes. Integrates with existing systems rather than replacing them, automating handoffs and document flows that previously moved through email.
- Workflow automation engine
- Document-flow integration
- Operational dashboards
Q2 2026
Internal staff portal — multi-association operations in role-based dashboards
Mid-market property operator · GCC region
Role-scoped portal for property managers, accountants, and maintenance staff. Reuses the OA data model from the management SaaS (zero duplication), adds multi-association switching, maintenance ticket lifecycle, financial reporting, and document storage tied to each association workspace.
- Next.js + tRPC
- NextAuth role-based access
- Drizzle ORM shared schema
Client identities withheld under engagement NDAs. Sector, geography, and scope are accurate. Full case studies on request.