Automations that learn the work, then get better at it.
Most teams have at least 20-30 annoying routine processes that everybody touches. We pick the ones eating the most time and build them as agents - single sequences, parallel runs, or an orchestrator with sub-agents - that run on triggers and proactively try to improve. They get better with each run.
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500+ hours a month, eating your team's week.
A team of 20 spends collectively 500+ hours a month on routine work - invoice triage, ticket categorisation, weekly status emails, meeting follow-ups, internal reports, task research, documentation and more. Everything that prevents your team from doing meaningful work.
Done well, automation does not replace anyone. It expands what your existing team can do. The senior people get their week back for the work only senior people can do. New hires get an onboarding co-pilot that already knows the company. Growth becomes a question of ambition again, not headcount.
Six things every engagement gets.
Discovery - the right work to automate
We spend the first two weeks finding the 5-10 highest-impact targets in your team's actual workload.
One automation, end-to-end
Built on your real systems, not a sandbox. Typically 4-6 weeks for the MVP. Designed as the template for the next five.
Reaches your real systems
Typed, role-aware tool wrappers around each system's API. Permissions enforced at the tool level - the agent literally cannot fetch what the calling user can't access.
Human-in-the-loop where it matters
Never auto-publish under your brand. Never auto-spend money. Never auto-decide on a customer. The approval gates stay yours.
Memory that compounds
The agent reads what went wrong last time before it acts this time. Lessons accumulate in a living document; the third run on a similar task is materially better than the first.
Security, in the architecture
Limited IP and device access. No public API surface. Tools enforce role at the tool level, not the prompt. Audit log + reasoning trace per run. Private-mesh deployment available.
MIT Study: 95% of organizations are getting zero return.
The split between the 5% extracting millions and the 95% stuck in pilot purgatory is driven by approach, not model quality, regulation, or talent.
Where the 95% get stuck - and what the 5% do differently.
The MIT study names the failure mode in plain terms: enterprise AI projects don't fail on model quality, regulation, or talent. They fail because the tools don't remember, don't adapt, and don't learn. We build for the other side of the divide.
- Tools that don't remember the user, the account, or the last conversation
- Workflow fit ignored - bolted-on chat layer, not woven into the work
- Built internally because it felt safer - internal builds fail at twice the rate of external partnerships
- Sales & Marketing budget chasing visible KPIs while the back-office ROI sits unclaimed
- Pilots demo well; production demands learning, and the tool plateaus
- Persistent memory - the agent on Tuesday knows what it learned about your account on Monday
- Continuous self-improvement - every recurring task accumulates a living lessons file
- Deep workflow integration - typed tools at the system level, not bolted on the chat
- Back-office wins (document automation, voice and call summarisation, code generation) where ROI actually lives
- Vendor partnership, not SaaS - co-evolved through real failures, not a feature checklist
Real systems running in production today.
Customer-support triage
Categorise incoming tickets, route to the right team, fetch the relevant context, draft a first response. A human approves before send. The agent learns which of its drafts you keep.
Invoice + AP processing
Read the PDF, extract line items, match against POs, flag anomalies, queue for approval. Books close faster; the AP person spends time on the cases that actually need judgement.
Weekly status reports
Pull from your project tools, your billing, your incidents. Draft a report in your voice, post to Slack on Friday afternoon. Senior people get Friday back.
Meeting → action items → tickets
Transcript becomes a list of decisions. Each decision becomes a ticket with the right assignee + due date inferred. The agent learns your team's conventions over time.
Sales lead enrichment
Take an inbound, enrich from public sources, score, route to the right rep with context already attached. Salespeople spend their week selling, not researching.
Recurring report generation
Monthly KPI digests, board-pack drafts, customer-health summaries. Triggered on a schedule, drafted in your voice, queued for review.
How an ERP-integrating team uses it.
19 skills in production cover the breadth of routine work - meeting summaries, ticket triage, finance queries, internal lookups across systems, daily reports, written summaries, and more. The whole team uses the agent like a teammate across Microsoft Teams and Telegram. Each skill keeps a living lessons file, so consecutive runs get materially better.
Three things people always ask.
What's a good first automation to try?
What if it gets something wrong?
How does the agent get better over time?
Bring us the work that's eating your week.
Diagnostic call walks through your current process and tells you which 2-3 are best candidates. Free; written summary either way.