AI that does real work.
We identify expensive, repetitive processes and build AI systems to automate them — agents that use your tools, workflows that run without babysitting, and assistants connected to your company's actual knowledge. Practical automation, not demos.
Reviewed
Hover or focus a component to see what it is and what it talks to. Arrow keys move between them.
Inbound documents and messages are ingested, an agent reasons with company knowledge and acts through the systems of record, and a person reviews the cases that need judgment.
Components:
- Inbound (email, PDFs, forms): The unstructured work arriving every day.
- Ingestion (extract, classify): Turns documents into structured fields with confidence scores.
- Agent (reasons, uses tools): A model with tools: it looks things up, decides, and acts — within limits you set.
- Knowledge (your docs): Company procedures and history, retrieved on demand.
- Systems of record (ERP, CRM): Where the work actually lands.
- Reviewer (exceptions): The person who sees what the agent was unsure about.
Connections:
- Inbound to Ingestion over email
- Ingestion to Agent over events
- Agent to Knowledge over REST, both directions
- Agent to Systems of record over REST
- Agent to Reviewer over handoff
Sound familiar?
- We're doing way too much of this manually.
- We want to use AI, but for something real.
What's included
8 services under AI & Intelligent Automation.
AI Agents
Willowark builds production AI agents with typed tool contracts, human review gates, and audit logs — software that reads, decides, and acts in your systems.
Learn more →Business Process Automation
Business process automation from Willowark: we map your workflow, automate the repeatable steps with code and AI, and keep people at the decisions that matter.
Learn more →AI Software Integration
AI software integration built for production: Willowark adds LLM features to your existing systems with structured outputs, evals, caching, and cost control.
Learn more →AI Document Processing
AI document processing that turns invoices, POs, drawings, and reports into validated structured data — with confidence scoring and human review built in.
Learn more →AI Knowledge Systems
Willowark builds AI knowledge systems on retrieval over your manuals, SOPs, and tickets — grounded answers with citations, permissions, and measured accuracy.
Learn more →AI Voice & Communication
AI voice and communication systems that answer calls, schedule, and route around the clock — built on your phone system, with human handoff engineered in.
Learn more →Engineering AI Automation
Engineering AI automation for manufacturers and OEMs: quoting from drawings, BOM extraction, spec compliance checks, and test documentation — engineer-reviewed.
Learn more →AI Prototyping & Proof of Concept
AI prototyping and proof of concept: Willowark tests your AI use case on real data in weeks and delivers honest accuracy and cost numbers before you commit.
Learn more →How we approach it
Straightforward, in this order.
01
Find the expensive process
We audit where hours actually go — re-typed data, document handling, reporting, triage — and score each candidate by frequency, cost, and how describable the pattern is. The best first automation is boring and high-volume.
02
Design around failure
Before any model runs, we decide what happens when it's wrong: human review gates where errors are costly, structured outputs with validation, and full logging so every result can be traced.
03
Build against your real systems
The automation connects to the tools you already run — email, ERP, spreadsheets, databases — rather than asking your team to move into a new one.
04
Measure, then widen the gate
We track time saved and error rates against the manual baseline. Review gates loosen only when the numbers earn it, and every pipeline keeps a kill switch.
Ask about AI & Intelligent Automation
Describe the problem. Get a straight answer.
One line is enough. An engineer replies within a business day.
Typical engagement
- Shape
- A short assessment to pick the first process, then a scoped build with review gates, then an optional retainer for widening the automation.
- Duration
- Proof of concept in a few weeks; a production automation usually one to three months depending on how many systems it touches.
- Team
- One lead engineer end to end, with a second engineer for integrations when the process spans several systems.
- You hold at the end
- Process audit with a ranked shortlist
- Working automation connected to your real tools
- Review queue, logging, and kill switch
- Runbook and handover session
- Pricing
- Scoped per project after a call; fixed-price phases where the scope is firm, time-and-materials where it isn't. How engagements work →
Work it out yourself
Is this process worth automating?
People, hours, loaded rate, error and rework cost, and how much of the work is actually automatable — against the build and running cost. Shows recoverable hours, payback, and the process shapes where automation fails.
How this gets priced
What moves the number, before there is a number.
We publish no rates — every engagement is quoted against a written scope. What we can tell you is what that scope will turn on, so you can see the shape of the price before the call.
Cost drivers
- How much of the process follows rules versus judgment
- Stability of the inputs — forms, formats, sources
- Which systems the automation must read from and write to
- How exceptions reach a person, and how fast
- Volume: the same build costs the same whether it runs ten times or ten thousand
A typical first phase
A working prototype on real documents or messages from your last month, measured against what people actually did. It ends with an accuracy figure, a list of the cases it could not handle, and a scope for production.
What makes it expensive
- Processes where every case is an exception
- Systems of record with no API
- Inputs that change format without warning
What makes it cheaper
- Starting with the one repetitive step everyone hates
- A clean hand-off to a person for anything below confidence
- Stable, structured sources
Who this is for
Built for operators, not for the demo.
Any business doing too much manually — from plant operations to back-office workflows in distribution, logistics, construction, and professional services.
Available as a scoped project, contract engineering, or a fractional arrangement — see how we work.
Common questions
Asked before every AI & Intelligent Automation project.
What business processes are good candidates for AI automation?
The best candidates are repetitive, rule-adjacent processes with clear inputs and outputs: re-typing data between systems, processing documents like invoices and forms, triaging email, drafting routine responses, and compiling reports. If a process is expensive, frequent, and follows a describable pattern, it is usually automatable.
Do we need our own AI team to run these systems?
No. We build automation with human review gates, monitoring, and clear failure behavior, then hand it over with documentation. Most systems need periodic review rather than a dedicated AI staff. When ongoing changes are expected, a retainer or fractional arrangement covers it.
How do you keep AI outputs reliable enough for business use?
By engineering around the model, not just prompting it: structured outputs with validation, retrieval from your actual documents and data instead of relying on model memory, human approval steps where errors are costly, and logging so every output can be traced and audited.
How long does an AI automation project take?
A focused proof of concept typically takes a few weeks; a production automation with integrations and review workflows typically runs one to three months depending on how many systems it touches. We scope the smallest version that produces real value first.
Which AI models do you use, and can we keep our data private?
We pick the model for the job — commercial APIs where quality matters most, smaller or self-hosted models where cost, latency, or data residency rule. Your documents and data are never used to train anyone's model, and we can keep processing inside your own cloud account or on your own hardware when that is a requirement.
What happens when the AI gets something wrong?
That case is designed in from the start: every automation has a defined failure path — a human review queue, a confidence threshold, or a safe default — plus logs that show exactly what the system saw and decided. An automation that fails loudly and predictably is worth far more than one that is usually right and silently wrong.
Strategy. Software. Systems.
Have a system that should exist?
Tell us what your operation is doing manually, what isn't connected, or what you're trying to build. We'll tell you plainly whether and how we can help.