AI features that add real product value — pragmatic, integrated, maintainable
Pragmatic AI features integrated into real products: data pipelines, embeddings/search, classification, automation — no hype.
AI work that behaves like product engineering
We build pragmatic AI features that integrate into real products — scoped, measured, and maintainable. Not experiments. Not demos.
Clear Objectives
We define what the feature needs to do before touching code — success criteria, edge cases, and what 'good' actually looks like for your context.
Realistic Data Constraints
We work with the data you actually have — auditing gaps, defining what's needed, and building pipelines that reflect real-world messiness, not clean demos.
Measurable Value
Accuracy, time saved, conversion lift, cost reduction — we agree on the metric before we build, so you know exactly what you're evaluating against.
Structured Data Pipelines
Raw data in, reliable outputs out. We design and build ingestion, transformation, and storage layers that hold up in production — not just in a notebook.
Classification & Tagging
Turn unstructured text, documents, or signals into structured, actionable categories — integrated directly into your existing workflow or product.
Automation & Decision Support
AI that removes manual bottlenecks — routing, triage, extraction, recommendation. Scoped to where automation adds real leverage, not where it adds risk.
We do not sell
Black-box AI with no defined outcome
General chatbots without a clear use case
Autonomous agents for high-risk domains
Experiments with no path to production
We define what the feature needs to do, what data it requires, and how it will be maintained — before any build begins.
Is this the right fit?
We work best with teams who are ready to scope properly — not those chasing AI for its own sake.
You have a clear use case — or you're willing to define one in a paid sprint.
You can access relevant data, or you want help creating a realistic data plan.
You want integration into a real product — web, app, or device.
You want AI as a marketing buzzword without a defined outcome.
Your project is in excluded categories — civil, medical/regulated healthcare, manned aviation.
You need niche autonomy research beyond pragmatic drones or mechatronics positioning.
Not sure where you fit? Book a free call — no commitment.
Heard any of these?
Tap a card to see how we approach it.
Not sure if your problem fits? Book a free 20-min call — no commitment.
Typical Deliverables
Concrete artefacts — not vague outputs.
Requirements and success criteria for the AI feature
What it does, how it's evaluated, what good looks like.
Data audit notes and data plan (if needed)
Gap analysis, sources, collection strategies.
Integration architecture and API/interface notes
Endpoints, contracts, data flows — how it plugs in.
Implemented feature (code) + deployment notes
Working, tested code your team can maintain.
Evaluation notes — what was tested, what's known
No false confidence. Known limits documented.
Documentation and handover
Written so your team owns it without us.
Exact artefacts vary by engagement type — confirmed during scoping.
How we engage
Three structured paths — choose based on how much clarity you already have.
Min. engagement
Contact us for scope →Blueprint Sprint
When scope / data is unclear
Define the use case, constraints, and approach before any build begins. The right starting point when you're not yet sure what AI can do for your problem.
Stages
Discovery
Constraints
Use-case definition
Approach doc
Typical patterns
Tool choice depends on the use case and existing environment. We'll confirm the stack during discovery.
Modern Data Pipelines & Storage
API-based Integration
Evaluation & Monitoring Basics
Secure Handling of Data & Credentials
Tools
Pattern
Priority
— Overview
Structured ingestion, transformation, and storage layers built for production — not just notebooks. Designed around your existing data environment.
— Tooling
Stack confirmed during discovery — always fits your environment.
Work we've done
Representative examples — not every project is public. More relevant work available on request.
fewer support tickets
Semantic Search for a SaaS Knowledge Base
— The problem
A B2B SaaS product was relying on basic keyword search across a large internal knowledge base. Users were missing relevant content, and support tickets were high.
— Outcome
34% reduction in support tickets within 6 weeks of deployment.
— What we built
Embedding-based semantic search pipeline, integrated into an existing Next.js product via a lightweight API layer.
— Stack / methods
less manual review
Document Classification Pipeline for Logistics
— The problem
A logistics company was manually reviewing hundreds of incoming documents daily — purchase orders, delivery notes, invoices — routing them to the right teams by hand.
— Outcome
Reduced manual review time by ~80%, with human-in-the-loop fallback for low-confidence cases.
— What we built
Multi-class document classification system with confidence scoring and a review queue for edge cases. Integrated into existing ops tooling.
— Stack / methods
How we keep
quality high
Quality isn't a final check — it's built into every stage. These four principles shape how every project runs, from the first brief to the final handover.
Define success criteria before building
We don't start writing code until we've agreed on what good looks like — accuracy thresholds, latency requirements, and the business outcome we're optimising for.
Build in stages with review points
Each milestone ends with a visible output and a review. You see what was built, ask questions, and sign off before we move forward. No surprises at handover.
Document assumptions and limitations
Every system has edges. We write down what we tested, what we assumed, and where the known limits are — so your team isn't flying blind when they take over.
Provide handover-ready outputs
Code, docs, deployment notes, and a walkthrough. The goal is for your team to own and maintain the feature without needing us in the room.
4
principles
100%
documented
Frequently Asked Questions
Everything you need to know before starting an AI features project.
Can you build an AI chatbot for my business?
Do you need access to our data?
How long does it take to develop an AI feature?
Can AI features integrate with our existing systems?
How do you ensure the AI system is reliable?
Still have questions?
Book a free discovery call→Let's build something
that actually ships.
Two ways in — a scoped estimate or a quick discovery call. Pick whichever fits where you are right now.
Get a Project Estimate
Tell us what you're building — we'll scope it and respond within 1 business day.
Book a Discovery Call
20 minutes, no obligation. We'll tell you honestly if we're the right fit.