Service
AI & Workflow Automation
Applying AI to the repetitive work that consumes hours a day — and being clear about where it does not belong.
What this service is
AI workflow automation means inserting a model into a business process to do the reading, classifying, extracting or drafting that a person currently does by hand, while the system keeps control of what happens next. It differs from traditional automation in that it handles unstructured input — an email, a PDF, a photographed invoice — that rule-based automation cannot parse.
The problem
What usually brings people here
Documents re-typed into systems
Invoices, purchase orders and forms arrive as PDFs and email attachments, and someone keys them in.
Requests routed by hand
A shared inbox where a person reads each message and decides who should handle it.
Answers buried in systems
Staff cannot get a straight answer out of their own data without exporting it and building a spreadsheet.
Classification that does not scale
Tagging, triaging or categorising high volumes consistently, where the rules are fuzzy and the volume keeps rising.
Capabilities
What we do
- Document processing and data extraction
- Classification, tagging and triage
- Task routing and approval workflows
- Retrieval over your own business data
- AI agents scoped to defined, auditable tasks
- Customer support and knowledge workflows
- Human-in-the-loop review and confidence thresholds
- Integration into existing applications and ERPs
Use cases
Typical engagements
Invoice and document intake
Extract line items, match against purchase orders, flag the exceptions and send only those to a person.
Inbox triage and routing
Classify incoming requests by type and urgency, route to the right queue, and draft a first response for review.
Question answering over business data
Let staff ask operational questions in plain language and get answers that cite the underlying records.
Delivery
How the work runs
- 01
Discovery & requirements
We map how the work actually flows today — the systems, the spreadsheets, the manual handoffs — and agree what success looks like in measurable terms.
- 02
Solution architecture
Data model, service boundaries, integration points and hosting. You get the shape of the system before anyone writes production code.
- 03
UX & technical design
Screens and flows for the people who will use it daily, reviewed against the architecture so the design is buildable as drawn.
- 04
Development & integration
Built in increments you can see and use. Integrations are wired early, because that is where most projects actually go wrong.
- 05
Testing & quality assurance
Automated tests around the logic that matters, plus migration rehearsals against real data shapes before anything goes near production.
- 06
Deployment & onboarding
Release pipeline, environment configuration, data migration and training for the teams who have to live with the system.
- 07
Support & continuous improvement
Monitoring, fixes and the next increment. Software that is used changes; the engagement allows for that.
Technology
What we build with
Backend & APIs
- Java 21
- Spring Boot 3
- Spring Cloud Gateway
- Spring Security
- Python
- REST & webhook APIs
Frontend
- React 19
- Next.js
- TypeScript
- Vite
- Tailwind CSS
Data
- PostgreSQL
- MongoDB
- Redis
- Flyway
- JPA
- Database-per-service
Messaging & storage
- Kafka / Redpanda
- Event streaming
- MinIO & object storage
- Dead-letter queues
Cloud & DevOps
- Azure
- Docker & Compose
- Kubernetes manifests
- GitHub Actions CI
- Static & container hosting
AI & automation
- LLM integration
- Retrieval over business data
- Document extraction
- Workflow orchestration
- Local & cloud inference
Before you commit
What to weigh up
AI is probabilistic. Build for the case where it is wrong: confidence thresholds, human review on anything consequential, and an audit trail showing what the model saw and produced. Do not automate a process nobody has agreed the rules for — AI will encode the existing confusion faster, not resolve it. And keep decisions with legal, financial or safety consequence with a named person.
Questions
Frequently asked
- What is AI workflow automation?
- Inserting a model into a business process to handle the unstructured reading, extraction, classification or drafting a person does today, while the surrounding system retains control of what action is taken.
- Which processes are good candidates?
- High volume, repetitive, unstructured input, and a clear notion of a correct answer. Document extraction, classification, routing and retrieval fit well. One-off judgement calls with legal or financial consequence do not.
- How do AI agents differ from traditional automation?
- Traditional automation follows rules you wrote and fails predictably when input does not match. An agent interprets intent and can handle variation, but it can also be confidently wrong — so it needs guardrails, bounded permissions and a review step that rule-based automation does not.
- Can AI be added to an application we already run?
- Usually. The practical requirements are that the data is reachable through an API or database and that the task is one a model is reliable at. We assess both before proposing anything.
- What are the risks?
- Incorrect output presented confidently, data leaving your environment, cost that scales with volume, and over-automating a process that still needed a person. Each is addressable, but only if it is designed for at the start.
- Does our data get used to train a model?
- Not in anything we build. Data handling is set at the architecture stage, including whether inference runs in your own environment or through a provider.
Our own products
Software we built on this
AI Interview Platform
Structured, evidence-based hiring decisions — without handing the decision to a model.
AI Demo Agent
Every interested prospect sees a real demo the same day, without waiting for a solutions engineer.
We use this capability on our own products, not only on client work. If one of them already does what you need, it will be cheaper than building it.
Related
You may also need
Custom Software Development
Software built around how your business actually works, rather than bending the business to fit a package.
API & System Integration
Connecting the systems you already run so data moves between them reliably instead of by spreadsheet.
Cloud & DevOps
Deployment pipelines, infrastructure and observability so releases are routine rather than an event.
Talk to us about AI & Workflow Automation
Tell us what you want to build, automate or improve, and we'll help you identify the right technical approach.