By function
Six places this work usually starts. For each one: the pain we hear, what we build in response, and the metric that tells you whether it worked.
The problem we are hired for
Nobody buys “AI”. Companies come to us because a process is eating their team alive, an earlier pilot died in a slide deck, or an auditor asked a question nobody could answer. Here is what we hear most often — and what we build in response.
The pain
Skilled people spend their week keying data from PDFs, emails and portals into systems that cannot talk to each other.
Hundreds of hours a month, and a close that slips whenever someone takes leave.
What we do
Document intelligence with per-field confidence, matching against your own records, and posting through the accounting API.
What changes
The keying stops. Exceptions — not every item — reach a human, in one queue with the mismatch shown.
The pain
Knowledge lives in three wikis and two people’s heads, so every repeat question is answered from scratch.
Slow first response, inconsistent answers, and support cost that grows with every new customer.
What we do
Retrieval grounded in your current documentation, with citations and an explicit refusal when the answer is not in the corpus.
What changes
Drafts arrive sourced and reviewable, so agents spend their time on judgement instead of searching.
The pain
The RPA bot that ran the process for two years broke when the vendor changed a button, and nobody noticed for a week.
Silent failures, manual catch-up, and a business that quietly depends on one fragile script.
What we do
Migration of brittle screen-scraping jobs to APIs and documented interfaces, in risk order, with alerts that fire on drift.
What changes
Automation that survives a UI change — and tells you when something upstream moved.
The pain
A pilot was bought last year. It demoed beautifully and never reached production, so nobody trusts automation now.
Budget spent, credibility lost, and a team that will resist the next initiative.
What we do
A fixed-scope pilot on your real data with an evaluation set, measured cost per item and an explicit go / no-go — including “no”.
What changes
Production is a decision with evidence behind it, not a leap of faith.
The pain
Nobody can say how the model behaves on edge cases, or whether last week’s change made accuracy worse.
Risk you cannot quantify — and no way to answer your auditor, your board or your customer.
What we do
A golden evaluation set from your documents, regression runs on every prompt or model change, and dashboards for accuracy, cost and drift.
What changes
Model changes are a reviewed pull request, not a surprise in production.
The pain
The team has automation ideas, a backlog and no capacity — every initiative competes with the day job.
Improvements stay on the roadmap for quarters while the manual work keeps compounding.
What we do
One engineer embedded with your team, shipping inside your repository and your review process, with a delivery lead accountable weekly.
What changes
Work lands in production while knowledge stays with your people, not with a vendor.
By function · detail
Every engagement starts with a metric we agree and baseline on your own data before a line of code is written. These are the ones that usually matter.
Invoices arrive by email and portal, get keyed in by hand, and the month-end close depends on three people remembering the exceptions.
Days-to-close and touchless invoice rate
Order intake, scheduling and status updates live in spreadsheets and inboxes, and nobody can say where a case actually is.
Cycle time per case and on-time rate
Leads sit in a queue for hours, CRM records are half-filled, and quoting is a copy-paste exercise across four tools.
Speed-to-first-response and data completeness
The team answers the same twenty questions all day and the knowledge lives in three people’s heads.
First-response time and resolution without escalation
Reviews are thorough but manual, the queue grows faster than the team, and findings are inconsistent between reviewers.
Review throughput and finding consistency
Work orders, parts and technician notes are coordinated by phone, and the office rebuilds the day every morning.
Jobs per technician per day and first-time fix rate
How we choose the metric
Automation projects fail when “better” is never defined. In discovery we pick a single operational metric with your process owner, baseline it on four weeks of your own data, and report against it every month.
Baseline first
Four weeks of real volumes, cycle times and error rates before anything is built.
One primary metric
Days-to-close, touchless rate, first-response time — not a dashboard of vanity numbers.
Guardrail metrics
Cost per item, latency and exception rate, so quality cannot be traded for speed silently.
Published monthly
A written report: what improved, what did not, and what we are changing next.
Straight answers
No “contact us for details”. If the answer is “it depends”, we say what it depends on.
Pilot in two to four weeks from kickoff, including discovery and blueprint. That assumes we get read access to a sandbox and a sample of real documents or tickets in the first week — that sample is the usual cause of delay.
Discovery is a fixed fee, the pilot is a fixed price, and production is a monthly retainer with a defined scope. We quote after discovery because the honest number depends on your process, not on a price list. If a process is not worth automating, we say so and charge only for discovery.
No. We integrate with what you run — ERP, CRM, ticketing, data warehouse, and the legacy application with no API. Replacing your core systems is a different project and usually a worse idea than automating around them.
Whatever the task and your constraints justify: frontier APIs where quality matters most, open-weight models you can self-host where data cannot leave your environment, and classical methods where a model is the wrong tool. We benchmark on your data during the pilot and document why we chose what we chose.
That is designed for, not discovered later. Every step has a confidence threshold; below it the item goes to a human with the evidence attached. Nothing with legal, financial or safety consequences is decided autonomously — see our Responsible AI Policy.
Next step
Say which function hurts and roughly how many items a month it involves. We will tell you whether automation is worth it — including when it is not.
One business day — and the reply comes from an engineer, not a sales sequence
