AI & Machine Learning

Who this is for

This service is for organisations that suspect AI could save time, improve decisions or open up something new, and want a practical partner to test that idea properly. Common starting points include:

• Large volumes of documents, emails or forms that people currently read and sort by hand.
• Staff who spend time searching manuals, policies or past records for answers.
• Historical data, such as sales, maintenance or usage records, that could support forecasts or flag unusual patterns.
• An existing product where users would benefit from search, summaries, recommendations or classification.
• A pilot that worked in a demo but is not yet reliable, measurable or affordable enough for real use.

You do not need a data science team. You do need someone who understands the work being improved and can judge whether the output is right.

How we approach a project

1. Frame the problem. We agree what the system should do, who uses the output, what a wrong answer costs and how success will be judged. This is written down before any model is chosen.
2. Look at the data. We sample real inputs, check quality and coverage, and confirm that using the data for this purpose is permitted.
3. Build an evaluation set. Before building the feature, we collect a set of real examples with agreed correct outcomes. Every later change is measured against it.
4. Try the simplest workable approach first. That may be a rule, an existing model through an API, or retrieval over your documents. Custom training comes in only if simpler options fall short.
5. Prototype with real users. A small group uses the prototype on real tasks. Their feedback shapes what we fix and what we drop.
6. Integrate and launch carefully. We add logging, cost controls, fallbacks and review steps, then release gradually.
7. Monitor and improve. Data and usage change over time. We track quality and cost after launch and re-run the evaluation whenever something changes.

Decisions we help you make

Is AI the right tool?

Some problems are better solved with clear rules, a better form or a cleaner database. We test this honestly at the start. If AI is not the right fit, a short assessment that says so is still a useful result.

Is the data ready?

Models reflect the data they are given. We look at whether data is complete, consistent, representative of the cases you care about, and labelled where supervised learning is involved. We also check where it came from and whether you have the right to use it this way.

Build, adapt or use an existing model?

Broadly, there are three routes:

• Use a hosted model through an API. Fast to start and capable across many tasks, but with ongoing usage costs, dependence on a provider and data leaving your environment.
• Run an open model yourself. More control over data and cost at scale, with more operational work and hardware to manage.
• Train or fine-tune a model. Worth it for narrow, high-volume tasks with good labelled data, or where existing models perform poorly on your domain.

We compare these on your evaluation set, your data constraints and your expected volumes rather than on general claims.

How will we measure quality?

We agree measures that match the task: correctness of extracted fields, how often answers cite the right source, how often the system should have declined to answer, and the cost per task. Quality is tested before launch and re-tested after any change.

Where do people stay in control?

We design review points where the cost of a mistake is high, make it easy to correct or override the system, and record enough to understand why an output was produced. A helpful structure here is the NIST AI Risk Management Framework, a voluntary framework released in January 2023, whose core is organised into four functions: govern, map, measure and manage. NIST has also published a generative AI profile to accompany it.

How is privacy protected?

We keep personal data out of prompts and training sets unless it is genuinely needed, mask or remove identifiers where possible, set retention limits on logs, and check the data terms of any third-party service. For organisations handling personal data in India, the Digital Personal Data Protection Rules, 2025 were notified on 14 November 2025 to operationalise the Digital Personal Data Protection Act, 2023, with an eighteen-month period for phased compliance. We factor these obligations into system design and work with your legal adviser on interpretation.

Security and quality built in

• Prompt and input handling. We treat user input and retrieved documents as untrusted, limit what the system is allowed to do with them, and test for attempts to make it ignore its instructions.
• Least privilege. AI features get access only to the data and actions their task requires.
• Source visibility. Assistants that answer from documents show where an answer came from, so users can check it.
• Repeatable evaluation. The same test set is run before every release to catch regressions.
• Cost and failure controls. Usage limits, timeouts and fallbacks stop a fault from becoming an outage or a large bill.
• Documented limitations. Known weaknesses are written down and shared with the people who rely on the system.

What we need from you to start

• A description of the task you want to improve and who does it today.
• Sample inputs and, ideally, examples of good outputs. Even a few dozen real cases help us judge feasibility.
• Information on where the data lives, who owns it and any restrictions on its use.
• Your constraints: data that must stay in a particular environment, budget for ongoing usage, and the systems the feature must connect to.
• A subject-matter expert who can review outputs and say what is right or wrong.
• A decision-maker who can agree the success measures with us.

From there, we can usually suggest a short assessment or prototype that tests feasibility on your own data before you commit to a full build.