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When off-the-shelf AI isn’t enough
General AI tools are impressive, but they are designed for everyone, which means they fit nobody exactly. If your work depends on your own data, your industry’s vocabulary, a particular sequence of steps or strict rules about what can and can’t happen, a generic assistant will get you part of the way and then stall.
A custom AI application is software built around your problem, with AI as one or more of its components. It has its own interface, its own data, its own rules and its own way of measuring whether it is doing a good job.
The kinds of problems we take on
- A manufacturer wanting a tool that reads customer drawings and specifications and prepares a first-cut quotation for an engineer to check.
- A real estate business that needs to match buyer requirements against its inventory and explain each match in plain language.
- A clinic group wanting internal tools that summarise patient history for doctors, strictly for review and never as a diagnosis.
- A coaching institute building a practice tool that generates questions from its own study material and explains answers.
- A SaaS startup whose entire product is an AI capability for a specific industry.
- A trading or finance team that wants research summaries from filings and reports, with decisions left to people.
What a full build covers
- Problem definition — what exactly the tool must do, for whom, and how success will be measured.
- Model selection — commercial APIs from OpenAI or Anthropic, open-weight models you can host yourself, or a mix, depending on quality, cost and data needs.
- Prompt, retrieval and pipeline design — how the tool gathers context, reasons and returns results.
- An evaluation set — real examples with expected outputs, built early and used to compare every change.
- The application — a web or mobile interface, user accounts, roles, history and admin controls.
- Infrastructure — hosting, databases, queues, monitoring, backups and cost controls.
- Documentation and handover — source code, deployment notes and guidance on maintaining quality.
How we work
We start small and prove the core idea before building the rest. The first phase is usually a focused prototype: the hardest part of the problem, tested against a small evaluation set of real examples. If results aren’t good enough, we find out quickly and cheaply, and discuss whether a different approach, better data or a narrower scope would help.
- Discovery — understand the problem, the users and the data available.
- Evaluation set — agree on examples and what a correct result looks like.
- Prototype — tackle the riskiest part first and measure it.
- Build — the full application in stages, with each stage reviewed.
- Launch — to a limited group first, with logging and feedback collection.
- Improve — use real-world feedback to expand the evaluation set and refine the system.
Technology
Backends are typically Python (often FastAPI) or Node.js, with PostgreSQL and pgvector for data and retrieval, Redis for queues and caching, and React or Next.js for interfaces. Models are called through official APIs or, for open-weight models, served on your own infrastructure or a GPU cloud provider. We deploy on AWS, Google Cloud, Azure or a VPS depending on your needs, and our cloud and server team can manage it afterwards.
What affects timeline and cost
- How hard the core AI problem is, which the prototype phase helps establish.
- Availability and quality of data for retrieval, evaluation or fine-tuning.
- Scope of the application around the AI — users, roles, integrations, reporting.
- Hosting model — commercial APIs versus self-hosted models, which trade setup effort against ongoing usage costs and data control.
- Compliance and data protection needs, including India’s data protection law and any sector rules that apply to you.
Mistakes to avoid
- Building the full application before proving the AI part works.
- Judging quality by a few impressive demos instead of a fixed evaluation set.
- Fine-tuning too early. Good retrieval and prompting often solve the problem more cheaply; fine-tuning is worth it only when they clearly don’t.
- No plan for model changes. Providers retire and update models; your system should make switching straightforward.
Frequently asked questions
Do we need our own data to build a custom AI tool?
Not always, but it helps. Many tools use your documents or records for retrieval, and almost every project needs real examples for the evaluation set. We assess what you have during discovery.
Should we host our own model?
Self-hosting open-weight models gives more control over data and can suit high, steady volumes, but it adds infrastructure work. Commercial APIs are faster to start with. We compare both for your case.
Who owns the application?
You do. The source code, prompts, evaluation set and documentation are handed over, and the system runs on infrastructure in your name or under your control.
What if the prototype shows it won’t work?
Then we’ve learned that before you spent the full budget. We’ll explain why and suggest options, such as narrowing the scope, improving data or using a different approach.
Can you maintain it after launch?
Yes. AI systems benefit from ongoing monitoring, evaluation updates and model upgrades, and we offer support arrangements for that.
Talk to us about custom ai applications
Purpose-built AI tools for your niche when nothing off the shelf does the job.