Capability 01
AI that survives contact with your operations
Most AI pilots work in a demo and fail in production. We build the workflow around the model: the approvals, the audit trail, the fallback when it gets something wrong. Then it ships.
How it runs
How one workflow moves from email to governed AI in production
- Person decides
- AI + human review
- Automated
- Documents moved by email
- Approvals in inboxes
- Checklists in spreadsheets
- Workflow in production
- Readable audit trail
- Threshold policy
- Cycle time dashboard
Pick one workflow
Partner onboarding, vendor verification or expense approval: one workflow that costs real money and a team that feels it every week. Not a platform strategy.
A person makes this call. The system gives them the context.
Time the current cycle
We trace each step and time it. The delay usually concentrates in verification and approval routing rather than in the work itself, and that finding sets the build order.
A person makes this call. The system gives them the context.
Rebuild with AI and review
A document agent extracts and cross-checks fields and scores its confidence. Anything below threshold goes to a named reviewer. Nothing passes without a high score or a human signature.
AI does the work, a named person reviews or signs off before it counts.
Route and log decisions
Onboarding, verification and approvals run as governed sequences. Each step knows its inputs, its owner and what to do when a check fails, and every decision is logged.
Rule-based and automatic, logged in the audit trail.
Instrument the result
A dashboard shows week by week whether the cycle got shorter and where exceptions cluster. Thresholds change only with evidence and a logged approval from the process owner.
A person makes this call. The system gives them the context.
Extend the pattern
Once one workflow is in production and the numbers are visible, the next one is faster because the review queue, the audit trail and the governance layer already exist.
A person makes this call. The system gives them the context.
What you get
7 deliverables, all yours to keep
- One workflow running in production
- Document and KYC agent with confidence scoring
- Exception queue with a named reviewer
- Audit trail your compliance team can read
- Threshold policy for machine versus human decisions
- Dashboard of cycle time and exception rate
- AI readiness assessment findings you keep
Sound familiar?
Why AI and digital transformation stalls before production
Your teams still move documents by email, verification takes days because a person opens every file, and the AI pilot stopped when compliance asked what happens when the model is wrong. That is the right question, and it is where we start. A model with a confidence threshold, an exception queue and a named reviewer is a production system.
We pick one workflow, time it, and rebuild it with AI where it holds and human review where it does not. A first workflow typically reaches production in 4–6 weeks. For a large-scale energy enterprise, that meant starting with verification and approval routing. The same governance layer runs through Partner & Vendor Operations.
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Verification takes days because a person opens every file
Each document is checked against a list by hand and forwarded to the next person, so cycle time depends on who is in the office.
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Nobody can say where a partner's application is
Approvals sit in inboxes with no status or owner, so the answer comes from asking around.
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The AI pilot stopped at the compliance review
The demo was impressive. Then compliance asked what happens when the model is wrong, and there was no answer.
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A chatbot nobody can audit
What you have seen so far produces answers without a record of how they were reached, so no one will put it near a regulated process.
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Internal approvals run on email and spreadsheets
Travel and expense approval, HR onboarding and procurement sign-off are checked by people who would rather be doing something else.
Services
What you can hire us for.
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AI Development
LLM applications, generative AI features and document agents built for production: structured outputs, pinned models, regression sets and a person on every consequential decision.
4–6 weeks -
AI Workflow Automation
Approvals, onboarding and verification rebuilt as governed workflows. AI handles volume, rules keep decisions repeatable, and people own the exceptions.
3–6 weeks -
Intelligent Document Processing
Document AI for invoices, KYC files and registrations. Per-field confidence, cross-checks against source data, and a reviewer queue for anything the model is unsure of.
3–5 weeks -
AI Consulting
AI strategy, readiness assessment and governance design from engineers who ship production AI, including EU AI Act readiness for systems that reach European users.
1–2 weeks
The work
Inside AI & Digital Transformation.
What our AI and digital transformation work covers
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Intelligent document and KYC processing
A document agent extracts and cross-checks the fields you care about, scores its own confidence, and routes anything below threshold to a person.
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Agentic automation for multi-step workflows
Onboarding, verification, approvals and exception handling run as governed sequences, each step with inputs, an owner and a defined path when a check fails.
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Process digitization
Partner, vendor, employee and customer journeys taken off email and spreadsheets, with a record, a status and an owner at every stage.
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AI readiness assessment
Where your data, systems and governance actually stand before anything is committed, including the honest finding that a given workflow is not ready.
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Governance design
Role-based access, decision logging, explainability and human sign-off on anything consequential, designed into the system rather than added after the audit.
How we work on AI in regulated operations
- 01
One workflow that costs real money, not a platform strategy
We start with something whose cycle time you can put a number on, so everyone can see whether it got shorter.
- 02
Time the cycle before building anything
Where the days go decides the build order. For a large-scale energy enterprise, the delay sat in verification and approval routing, not in the work.
- 03
Automation where it holds, review where it does not
The model handles the repetitive, high-volume, well-defined part. People handle exceptions, judgment calls and sign-off.
- 04
Working software every week
One-week sprints with a demo every week: a verification step on real documents, an approval route on your hierarchy, an exception queue with real cases. Scope can move; dates do not.
- 05
Telling you when it will not pay back
The readiness assessment and cycle-time instrumentation are yours either way. When they show a planned automation would not pay back, we say so.
Where AI automation goes wrong, and how we avoid it
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The documents the model gets wrong flow through unchecked
A confidence threshold, an exception queue and a named reviewer. Nothing passes without either a high score or a human signature.
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Governance is bolted on after the first audit
Role-based access, decision logging and sign-off on consequential steps are designed in from the first sprint.
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Nobody can tell whether the automation helped
The workflow is instrumented from day one, with cycle time and exception rate visible week by week.
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Each new workflow starts from nothing
The review queue, audit trail and governance layer are built once and reused, so the second workflow goes faster.
Team and timeline
4–6 weeks
to a first AI use case live, human-reviewed
Indicative. Actual duration depends on requirements and complexity, and can change.
Who works on it
- Solution architect
- AI engineer
- Full stack engineers
- QA engineer
- Delivery lead
Engagement model
Agile Time & Material for evolving scope. Fixed Cost where the workflow is well understood.
Good to know
A readiness assessment, where you want one first, typically takes 1–2 weeks. The build plan assumes access to systems and sample documents in week one, and reviewer time from your team early on.
Proof
Engagements behind the numbers.
How a leading energy enterprise reduced partner onboarding time by 45%
faster partner onboarding
How a leading construction enterprise improved vendor processing by 42%
faster vendor processing
How a large global financial services company reduced travel processing time by 38%
faster travel processing
FAQ
AI & Digital Transformation: common questions.
Where should we start with AI and digital transformation?
With one workflow that costs you real money and has a cycle time you can measure, such as partner onboarding, vendor verification or expense approval. We time the current cycle, find where the days go, and rebuild that workflow first. A platform strategy can follow once one workflow is in production and the numbers are visible to everyone.
What do you need from our team during the build?
A process owner who can make decisions about the workflow, a sample of real documents including the awkward ones, and access to the systems the workflow touches. Early on, reviewers spend more time in the exception queue while thresholds are tuned, and that time falls as the model settles. Your compliance team should see the design before the first release, not after it.
What happens when the model gets something wrong?
It is designed for. Every extracted field carries a confidence score, fields are cross-checked against other data, and anything below threshold goes to a named reviewer. Nothing passes without either a high score or a human signature. A documented threshold policy explains exactly when a machine decision stands and when a person must confirm it.
Will our compliance team accept it?
They get what stopped most pilots: an answer to what happens when it is wrong. Role-based access, decision logging, explainability and human sign-off on consequential steps are designed into the system. The audit trail is written for compliance readers, and AI-generated changes to the code are logged against who approved them.
What if a workflow is not ready for automation?
Then we tell you. The AI readiness assessment looks at where your data, systems and governance actually stand before anything is committed. Sometimes it shows a planned automation would not pay back. You keep the findings and the cycle-time instrumentation whether or not the program continues, and we count that honest answer as a good outcome.