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AI WORKFLOW AUTOMATION
Make AI useful inside the work your team already does.
We build controlled workflows that understand emails and documents, retrieve business context, prepare work for review and update the systems you already use. People remain responsible for decisions that require judgement.
Enquiries · Documents · Knowledge · Research
CLOUD PROGRAMME
AWS Activate
Programme member.
DELIVERY MODEL
Founder-led delivery
Direct access from scope through production.
PRODUCTION OWNERSHIP
Build and operate
Support and improvement beyond launch.
WHERE AI FITS
Most operational work is a chain of small decisions.
Work arrives through email, documents and forms. Employees interpret it, find relevant context, handle exceptions, prepare an answer and update several systems. We automate the repetitive preparation while preserving the decisions that need an accountable person.
Enquiry and quotation workflows
Turn incoming requests into reviewable commercial work.
Read enquiries and attachments, identify missing details, retrieve customer and pricing context, prepare a quote or response and route it for approval.
EMAIL + FORMS → STRUCTURED ENQUIRY → APPROVED RESPONSE
Document intake and validation
Process documents without hiding incomplete or conflicting information.
Classify forms, invoices and supporting evidence; extract fields; validate completeness; match records and route exceptions.
DOCUMENTS → VALIDATED RECORD → EXCEPTION QUEUE
Internal knowledge and operations
Give employees reliable answers from approved business information.
Retrieve relevant policy, product and project information with citations, access controls and a clear escalation route.
QUESTION + CONTEXT → CITED ANSWER → RECOMMENDED ACTION
Research and account intelligence
Turn fragmented research into structured, reviewable evidence.
Combine websites, directories, CRM records and internal criteria to produce evidence briefs, scores and recommendations.
MULTIPLE SOURCES → EVIDENCE BRIEF → HUMAN DECISION
SELECTED AI WORKFLOW EXAMPLE
A complete research workflow, not an isolated AI prompt.
This internal production system is included as a concrete example of the service approach: several sources connected, evidence retained and consequential action kept under human control.
Supporting case study · Internal production system
Account Intelligence
We built an internal system to replace repeated company research across specialist directories, company websites, corporate records and CRM data.
The workflow gathers evidence, classifies operational fit, identifies relevant decision-makers and prepares a recommendation. Every conclusion remains linked to its source, and a person reviews the account before any external action.
- Multiple sources connected
- Directories, websites, company records and internal data joined into one workflow.
- Evidence retained
- Every score and recommendation remains linked to the supporting information.
- Human action controlled
- The system can prepare research and outreach, but it cannot contact a company without approval.
CONTROLLED BY DESIGN
Automate the preparation. Keep people accountable for the decision.
The level of automation should follow the consequence of an error. Routine preparation can run automatically. Low-confidence, unusual or high-impact work is held for review with the supporting evidence visible.
RFQ-1048
Commercial review
Source evidence
Pricing follows the 2026 service schedule. Variances above 8% require commercial approval.
Policy · 4.2Proposed action
Prepare the renewal with the current schedule and route the pricing variance to the commercial owner.
Approval, evidence and outcome saved to the audit trail.
Routine automation
- Extraction
- Classification
- Record lookup
- Deterministic validation
- Draft preparation
- Approved system updates
Human review
- Low-confidence matches
- Missing evidence
- Commercial exceptions
- External communication
- High-value actions
- Final submission
Operational evidence
- Representative test cases
- Input and output history
- Approval records
- Failure monitoring
- Cost and latency
- Versioned changes
Validated outputs
Important data must match an expected structure before systems are updated.
Evaluated behaviour
Representative cases test quality and regression before changes are released.
Recorded decisions
Inputs, recommendations, approvals and actions remain inspectable.
Controlled access
People and automated processes receive only the information and permissions they need.
Exact calculations remain deterministic. Consequential decisions remain with the appropriate person. AI is used only where it improves the workflow.
WAYS TO START
Begin with one workflow and the evidence needed to control it.
- 01
Assess
Best for
A repeated process has been identified, but its value, feasibility or control requirements are not yet clear.
What we do
Map the inputs, systems, decisions, exceptions, owners and current cost of the workflow.
WORKFLOW MAP · OPPORTUNITY ASSESSMENT · RECOMMENDED FIRST PROOF
- 02
Prove
Best for
Extraction, retrieval or AI behaviour needs to be tested against realistic examples before a production investment.
What we do
Build the difficult part and evaluate it against representative cases.
WORKING PROOF · EVALUATION SET · MEASURED FINDINGS
- 03
Implement and operate
Best for
The value and control model are sufficiently understood to connect the workflow to live systems.
What we do
Build the complete workflow, integrations, review route, monitoring and production controls.
PRODUCTION WORKFLOW · DOCUMENTATION · SUPPORT AND IMPROVEMENT ROUTE
ADDITIONAL WORK
AI supporting a controlled product workflow.
ZAPFOLDER
Building an AI-assisted review and approval platform.
Creative operationsCustom softwareAI workflows
Live B2B SaaS with sharing, versioning, approvals, billing and production AI operations.
View case study
GKM TECH
Combining computer vision and expert review for goalkeeper analysis.
SportComputer visionCustom software
Customer platform, video-processing backend and specialist annotation workflow.
View case study→
Map an AI workflowHAVE SOMETHING SIMILAR IN MIND?
Start with one workflow that consumes too much skilled time.
FAQ
AI workflow automation questions
Yes. AI workflows are most useful when they work with the systems already involved in the process, including email, document stores, CRM, ERP, communication tools and specialist platforms. Provider selection remains flexible and is based on capability, security, cost, latency and your existing agreements.
We define representative cases and task-specific criteria before production release. Measures can include extraction accuracy, classification quality, missing-information detection, citation quality, false actions, latency and the proportion of work requiring review.
The workflow can withhold an action, request missing information, route the case for review or escalate it to an accountable owner. The correct behaviour is designed around the consequence of the error, with evidence and decisions retained for investigation.
Not necessarily. A focused first phase can establish what data exists, what can be used safely and where quality limits the proposed workflow. Representative real examples are usually more useful than a theoretical data-cleaning exercise.
We can build systems that select tools and perform multi-step work, but autonomy is not the objective. We decide which actions can run safely, which need review and which should remain deterministic.
PROJECT INTAKE / AI WORKFLOW
Bring us one workflow that consumes too much skilled time.
Tell us what arrives, what employees do with it, which systems they use and where exceptions occur. We will help determine what can be automated safely and what a sensible first proof should demonstrate.