Operational AI · human-approved
Pipelines that read documents, check them against your live data and do the ERP work, with a person approving anything above your limits.
The thesis
Isolated AI (the usual way)
Grounded operational AI (our way)
Live pipeline
Vendor emails a PDF to a shared inbox, where it waits until someone opens it.
The invoice arrives by email or upload and extraction starts straight away.
A clerk re-types line items and totals into the accounting system. Typos creep in.
AI extracts every field with a confidence score. Low-confidence fields go to a person.
The clerk hunts through warehouse paperwork to confirm the goods arrived.
Each line is matched against the purchase order and goods receipt in the ERP.
An approval email sits in a manager's inbox while the vendor chases payment.
Invoices above your threshold go to an approver in one click, then post to the ledger.
Operational Capabilities
Click on any topic below to open its dedicated capability page, architecture pipeline, live simulation, and metrics.
Supervised customer-facing agents grounded in your ERP and knowledge base.
First-response resolution grounded in your knowledge base and ERP.
Natural voice triage that never puts callers on hold.
WhatsApp, email, web chat, and SMS in a single connected queue.
Agents that qualify leads, book meetings and watch your reviews around the clock.
Enrich, score, and route inbound leads in seconds, not days.
Zero email ping-pong. Meeting slots confirmed in a single exchange.
Monitor customer sentiment, draft tailored responses, and catch detractors early.
Back-office transactional automation reading documents and posting to GL ledgers.
Extract PDF invoices, verify against POs and receipts, post to ledger.
Instant, cited answers from your SOPs, technical manuals, and ERP history.
Catch order holds, credit breaches, and delivery variances instantly.
Operational AI
The model reads. Rules decide. A person approves.
Prices, tax and stock are computed by code, never by the model.
Talk to us about a pilotFour automation hubs
Turn PDFs, scanned bills of lading, delivery notes and supplier invoices into validated records mapped straight into your ERP.
Confidence score per field · Human review below threshold
Rules that watch live operational signals and make low-risk, high-frequency choices inside limits you set.
Hard limits · Every decision logged
Agents that read inbound channels, classify what the customer needs and draft replies grounded in your ERP data.
Immediate first response · Human handoff when unresolved
Integrations that catch API failures, reconcile records that drift out of sync and retry without anyone re-keying.
Idempotent delivery · Dead-letter queueing
ROI calculator
Move the sliders to estimate the hours and cost.
People doing data entry, order booking, invoice matching or stock spreadsheets.
Copying between email, spreadsheets, PDFs and your ERP.
Salary, taxes, software seats and workspace overhead.
Time finding lost records, fixing prices or issuing credit notes.
Projected annual recovery
$0
Estimated net annual saving
Indicative only. Assumes automation captures 75% of the manual hours, 50 working weeks a year, and an implementation cost that scales with team size. Discovery replaces these assumptions with your measured baseline.
Scope your automation blueprintGuardrails
Every pipeline has configurable sign-off thresholds. Invoices above your limit, or unusual discounts, wait for a named approver.
Your pricing, supplier terms and operational data are not used to train models. We use providers and settings that retain nothing.
Model output is checked against a strict schema before it touches your system. Anything that fails goes to a review queue, not the ledger.
For regulated operations we run open-weights models inside your own cloud account or on your servers.
Approach map · AI automation
Two weeks in shadow mode before the agent acts on anything.
We watch the manual process end to end and measure how often it runs, how long it takes and where it goes wrong.
Automating a process that should have been removed instead.
We rank every automation candidate by return and feasibility so you fund the ones that pay back first.
Spending on automations with no measurable return.
We define triggers, knowledge sources, confidence thresholds and the points where a human must approve.
An agent taking an action in front of a customer that nobody approved.
We build the connectors, load your documents and policies into the knowledge base and integrate with the systems it acts on.
Rate limits and expired credentials breaking the flow.
The automation runs alongside your team on real work without taking any external action, so we can compare its decisions with theirs.
Real-world inputs the test set did not cover.
The automation goes live with a human approving low-confidence cases and monitoring switched on.
Silent failures going unnoticed.
We tune thresholds as volume grows and move to the next item on the opportunity map.
Accuracy drifting as your business changes.
FAQ
Accuracy, data handling and what happens when the model is unsure.
Ask us directlyNot without a person seeing them first. Each workflow has a confidence threshold agreed during design. Below it, the draft is held for a named person to approve. During the two-week shadow stage the agent takes no external action at all, so we can compare its decisions with your team's before anything goes live.
No. We use providers and settings that do not retain or train on your data, and for regulated operations we can run open-weights models inside your own cloud account or on your servers.
Sample documents and transaction logs, time with the people who do the work today, and current hourly cost estimates for the roles involved. The approach map above lists what each stage needs.
Not always. If your core system has usable APIs and reasonably clean data, we automate on top of it. If it does not, we say so, because automation on scattered data tends to stall before production.
Next step
Book a 30-minute call with an automation engineer. We review your repetitive workflows and map the first pipeline, including where a person must stay in the loop.
First call: 30 minutes · A senior consultant, not a sales rep
