Service
Custom AI Applications
Generative AI, grounded in your knowledge.
Every business has unique challenges. We build custom AI applications that transform ideas into practical solutions, helping organizations improve experiences, uncover insights, and create new opportunities.
5.0 on Clutch24+ AI application projects deliveredReply within 1 working day
Overview
AI agents do tasks for your team; AI applications are products your users use.
Most AI features fail for one of two reasons: they hallucinate because they're disconnected from real data, or they're a thin wrapper around a model that never quite fits the way your business works. We build the opposite — applications grounded in your own knowledge base with retrieval (RAG), so answers are accurate, current, and cited back to the source a user can verify.
A custom AI application is more than a model behind a chat box. It's the interface your team actually wants to use, the retrieval and data pipeline that keeps it grounded, the evaluation harness that proves it's right, and the integration that makes it a natural part of an existing workflow. We design and build all of it, end to end, and tune it to your domain rather than forcing your domain to fit a generic tool.
Whether it's an in-product copilot, a voice agent for support and sales, a document-intelligence pipeline that reads contracts or claims, or a research assistant over your internal data, we ship a production application with the accuracy, security, and observability to back it. You get something your team trusts and your customers feel — not a demo that wins a meeting and disappoints in practice.
Capabilities
What we deliver.
RAG & knowledge grounding
Retrieval pipelines over your documents and data so answers are accurate, current, and cited — with the plumbing to keep the knowledge base fresh as your content changes.
Copilots & assistants
In-product AI that helps users do their job faster, embedded where they already work and aware of the context they're working in.
Voice & chat interfaces
Natural voice and chat experiences for support, sales, and internal operations — with memory, escalation, and a tone that fits your brand.
Document intelligence
Extraction, classification, and summarisation pipelines that turn contracts, forms, and unstructured files into structured, searchable data.
Custom model & fine-tuning
Where off-the-shelf models fall short, we fine-tune or compose models around your domain language, formats, and edge cases.
Evaluation & accuracy
Test sets, scoring, and human review built in, so you can measure quality, catch regressions, and improve the system with confidence.
Who it's for
Built for teams like yours.
- SaaS companies adding AI features
- Businesses with large document sets
- Teams wanting a chatbot or voice agent
Use cases
What we build with it.
In-product copilot
An assistant inside your product that understands the user's context and helps them get work done faster.
Knowledge-base chatbot
Answers grounded in your docs, policies and help centre — with citations, so users can trust them.
Voice agent
Natural phone or in-app voice conversations for support, bookings and sales, with handoff to a person.
Document extraction
Turn contracts, forms and invoices into structured, searchable data your systems can use.
Content generation
On-brand drafts, summaries and descriptions at scale, with review steps built in.
Case studies
Related work.
How we work
From first call to launch, in four steps.
Step 1
Discover
A free call to understand your goals, users and constraints.
30 minutes, freeStep 2
Scope
A written proposal with features, timeline and fixed price.
Within 1–2 daysStep 3
Build
Weekly sprints with a demo every week. You see real progress, not status reports.
Typically 4+ weeksStep 4
Launch & support
Go-live, monitoring and ongoing improvements.
Ongoing
Pricing & timeline
Clear scope. Clear price.
Best when
You want an AI feature or product your customers will use every day
Typical timeline
6–10 weeks to a production-ready first version
“Strong stakeholder management and a real drive to help customers succeed. Their automation work cut our processing time in half and fixed workflows we'd struggled with for years.”
How do you keep generative AI accurate?
We ground models in your own data with retrieval (RAG) so responses are based on real sources, add evaluation and guardrails, and measure accuracy against test cases before anything ships. Where it helps, answers cite the source so users can verify them — accuracy you can audit, not just hope for.
Do we need a huge dataset to start?
No. Many high-value features work immediately with LLMs and your existing documents, tickets, and workflows — no training data required. Where a custom or fine-tuned model genuinely adds value, we'll tell you exactly what data is needed and whether the payoff justifies it.
Can you embed AI into our existing product?
Yes. We build AI features as a clean, well-documented part of your current stack — proper APIs, sensible boundaries, and your design system — so it feels native to the product rather than a bolted-on widget.
Which AI models do you use?
We're model-agnostic and choose based on the job, your accuracy needs, data-privacy requirements, and cost — frontier models like Claude and GPT, open models you can self-host, or a mix. You're never locked to a single vendor, and we can swap models as the landscape changes.
How do you handle data privacy and security?
Your data stays within boundaries you control. We support private deployments, scope what the application can access, avoid training third-party models on your data, and design to the compliance requirements your industry demands.
More services
Let's design the AI your users will actually use.
Book a free AI strategy call. We'll talk through your idea, your data, and the fastest way to a version that works in production.