Automate the work nobody wants to do twice.
Classification, extraction, routing and drafting handled automatically — with confidence thresholds that escalate to a person when the system is unsure.
- Workflow agents
- Document parsing
- Human-in-the-loop
Document, support and back-office workflows automated with human review built in.
Automation with an escape hatch.
Full autonomy is rarely the right target. The reliable pattern is high-confidence automation plus a review queue, which lets you raise the automation rate as accuracy proves out.
Why it matters
Teams lose hours every week to copying data between systems and chasing approvals. Automation only pays off when the exceptions are handled as carefully as the happy path.
What we build
Document and email processing, approval workflows, data enrichment, internal agents with tool access, and scheduled orchestration across your systems.
Who it is for
Operations, finance and support teams with high-volume repetitive work and existing tools that do not talk to each other.
What we handle from strategy to delivery.
Six areas we take responsibility for on ai automation engagements — no handoff gaps between them.
- 01
Workflow analysis
Volume, exceptions and true cost of each manual step.
- 02
Automation design
What is deterministic code and what genuinely needs a model.
- 03
Orchestration development
Durable workflows with retries, timeouts and idempotency.
- 04
System integration
CRMs, inboxes, storage, spreadsheets and internal APIs.
- 05
Human review interfaces
Queues where a person confirms low-confidence output.
- 06
Monitoring & iteration
Throughput, accuracy and exception dashboards after launch.
Standards we hold every ai automation project to.
- Hours recovered
- Repetitive work removed
- Fewer errors
- Validated, logged runs
- Durable workflows
- Retries and safe replays
- Full audit trail
- Every action traceable
From first conversation to a product that is ready to grow.
01 — Discover
We map the workflow, the data available and the measurable outcome AI is supposed to improve.
We review requirements, existing analytics, competitors and user needs to understand where ai automation will create the most value. Nothing is proposed before the problem is clear.
Deliverables
- Use-case assessment
- Data inventory
- Success metrics
Typical activities
- Workflow interviews
- Data review
- Feasibility check
Success criteria
A clear, shared understanding of the problem, scope and expected outcome.
02 — Define
We decide what should be deterministic software and what genuinely benefits from a model.
We turn research into a clear product direction, priorities and information architecture. Scope, sequencing and technical direction are agreed in writing before work starts.
Deliverables
- Solution design
- Evaluation criteria
- Cost model
Typical activities
- Architecture design
- Provider comparison
- Risk assessment
Success criteria
Everyone understands what is being built, in what order, and why.
03 — Design
We design the product surface so people can understand, correct and trust the output.
We translate the agreed structure into a polished, responsive interface — every state, breakpoint and edge case included, reviewed together as we go.
Deliverables
- Interaction design
- Review and override flows
- Prompt or model spec
Typical activities
- Flow design
- Guardrail definition
- Stakeholder review
Success criteria
The experience is validated and ready for implementation.
04 — Build
We implement the pipeline, integrations and interfaces with cost and latency treated as requirements.
We turn approved designs into production-ready software using maintainable components and a scalable architecture. You see working software throughout, not just at the end.
Deliverables
- Working pipeline
- System integrations
- Guardrails
Typical activities
- Development
- Prompt or model iteration
- Integration testing
Success criteria
The product works reliably across the required devices and scenarios.
05 — Validate
We score the system against a real evaluation set instead of relying on impressions.
We test the product against the real requirements, profile performance and surface issues before launch rather than after it.
Deliverables
- Evaluation results
- Regression test set
- Accuracy baseline
Typical activities
- Test-set construction
- Scoring runs
- Failure analysis
Success criteria
Critical issues are resolved and the product is ready for launch.
06 — Launch
We release with monitoring for quality, spend and drift, then iterate on real usage.
We deploy, review the built product in production and refine the details that only appear in the real thing. Monitoring and handover happen at the same time.
Deliverables
- Production deployment
- Monitoring dashboards
- Feedback capture
Typical activities
- Rollout
- Cost tuning
- Continuous evaluation
Success criteria
The product is live, verified, documented and ready for users.
Everything handed over, nothing locked away.
Concrete output at the end of a ai automation engagement — code, assets and documentation you own.
The stack we reach for first.
Chosen per project constraints — this is the starting point, not a rule.
Backend
- Python
Data
- PostgreSQL
AI
- LangGraph
- Temporal
- OpenAI
- Zapier
AI products that do real work.
Selected projects built with the same approach, team and standards.
AI Background Remover SaaS
SmartBG Remover
A subscription SaaS that removes image backgrounds in seconds, with batch processing and an API for developers.
- Next.js
- Python
- AWS
Doctor's Management System
Pocket MD
A clinic management system covering appointments, patient records, prescriptions and billing in one workspace.
View project
Online Learning Solution
E-Learning Platform
A course platform with video lessons, progress tracking, assessments and instructor analytics.
View projectOutcomes, not just output.
Clients stay because the work reduces risk and cost after launch, not only because it looks good at handover.
- 01
Less rework
Exceptions are modelled before automation goes live.
- 02
Faster decisions
Work moves through queues instead of inboxes.
- 03
Better performance
Throughput measured, not assumed.
- 04
Clear handoff
Runbooks for owners who are not engineers.
- 05
Long-term thinking
Composable steps that survive tool changes.
Questions we get asked.
Still unsure about something on ai automation? Ask us directly — we answer honestly, even when the answer is no.
Low-confidence cases never auto-execute — they route to a review queue, and every action is logged for audit and rollback.
Automation rate, accuracy against a labelled sample, handling time saved and cost per processed item.
Yes, we deploy into your cloud account when data residency requires it.
With one high-volume, well-understood workflow — measurable value in weeks builds the case for the next one.
It routes to a human review queue with context, rather than guessing.
Yes, via APIs, webhooks and, where necessary, scheduled data exchange.
Often delivered together.
AI Integration
Large language models embedded into your product with guardrails and evaluation.
View serviceCustom Software
Bespoke systems for workflows that off-the-shelf tools cannot model correctly.
View serviceAI/ML Solutions
Custom models for prediction, scoring and recommendation, deployed and monitored.
View service
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Tell us what you are trying to build. We'll help you figure out the right next step — scope, sequence and what it realistically takes.
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