Document intelligence
Layout-aware extraction, table understanding, multi-document reasoning, and structured output validation.
Solutions / AI Automation
Workflow AI that earns its keep.
We've packaged the patterns we use over and over — document extraction, ticket triage, contract review, internal copilots — into a solution accelerator that gets you from ambition to production in 6-10 weeks.
8 wks
Median time to production
87%
Median accuracy on customer eval sets
63%
Cycle-time reduction (median)
ROI < 6mo
Across recent engagements
What it is
The work, plainly described.
AI Automation is our solution accelerator for the AI use cases that show up in almost every business: document understanding, agent-driven workflows, internal copilots, and ticket-triage automation. We've built the foundations — retrieval, eval, observability, fallback — so you can focus on the workflow-specific configuration. The result is faster time-to-value with the same engineering rigor.
Where it fits
- Document-heavy operationsClaims, contracts, SOWs, applications, regulatory filings — anywhere humans read documents and extract structured data.
- Internal copilotsCustomer support, sales enablement, IT helpdesk — where employees spend time looking up information.
- Ticket triageInbound classification, prioritization, and routing for support, sales, or operations queues.
- Process automationMulti-step workflows where AI agents can replace or accelerate human-in-the-loop steps.
Capabilities
What we'll actually do.
Each of these is a deliverable category, not a buzzword bullet. We scope, build, and stay accountable for each one.
Agentic workflows
Bounded-action agents with dry-run mode, audit logging, and human-in-the-loop checkpoints.
Internal copilots
RAG-powered assistants over your documentation, runbooks, or product data with eval-tested response quality.
Triage & classification
High-volume classification with active learning, confidence scoring, and human-review queues.
Eval & observability
Eval suite, regression detection, and the dashboards that show whether the AI is actually working.
Safe AI patterns
PII redaction, prompt injection defense, response filtering, and the safety patterns that keep enterprise AI deployable.
Process
How an engagement actually runs.
No mystery, no shifting goalposts. Five phases with measurable outcomes per phase.
Use-case scoping
Two-week scoping engagement. We define the workflow, build a real eval set, and propose an architecture.
Foundation deployment
Retrieval, eval, observability, and the workflow scaffolding deployed to your cloud account.
Workflow build-out
Workflow-specific logic, prompts, integrations, and UI.
Pilot & validation
Shadow traffic, then a real cohort. Live metrics, weekly review.
Sustained operation
Monthly model upgrades, eval expansion, and continued workflow improvements.
Why us
Three things you should know.
Accelerator + engineering, not just accelerator
We don't leave you with a templated app. The engagement includes the engineering work to integrate with your stack.
Eval-driven from sprint zero
Every workflow ships with a measurable eval set. If it can't be evaluated, we won't build it.
Production patterns, not demos
Our accelerator includes observability, safety, and ops — not just the happy-path demo.
Frequently asked
The questions everyone asks.
How is this different from buying an off-the-shelf AI tool?
Off-the-shelf tools work for generic use cases. Our accelerator gets you the speed of a tool with the customization of an in-house build. We deploy to your cloud, integrate with your stack, and you own the result.
Do you support on-prem or air-gapped deployment?
Yes — we've deployed AI workflow accelerators in air-gapped and on-prem environments using self-hosted Llama and Mistral models.
What models do you use?
We're model-agnostic. We pick what fits your latency, cost, and compliance constraints — OpenAI, Anthropic, Bedrock, Vertex, Azure OpenAI, or self-hosted.
How do you measure ROI?
We define 2-4 outcome metrics at scoping (cycle time, error rate, cost per transaction, deflection rate) and track them in dashboards through and past launch.
What happens after you leave?
Documentation, runbooks, and a structured handoff. Your team owns the deployed workflow. Optional retainer for monthly model upgrades and eval maintenance.