LLM assistants, document automation and predictive models wired into your stack
AI that does a specific job in your business and can be measured - assistants grounded in your own documents, document and workflow automation, and predictive models wired into the systems you already run.
The gap between a demo and something you'd let a customer touch is entirely in the engineering around the model: retrieval that finds the right source, prompts that refuse when the answer isn't there, evaluation that catches regressions, and guardrails on what it's allowed to say. We build that layer. The model is the easy part and it's replaceable - everything that makes it trustworthy is what we're actually delivering.
Answers grounded in your documents, policies and product data, with citations - and a refusal when the source genuinely doesn't cover it.
Invoices, forms, contracts and emails read, classified and pushed into your CRM or ERP. The manual re-typing simply stops.
Extraction from scans, photographs and handwriting, and image classification for quality control or cataloguing.
Demand forecasting, churn scoring and lead prioritisation trained on your history, delivered as an API your systems call.
A test set built from real questions, accuracy tracked over time, PII redaction and topic limits. You get numbers, not vibes.
Slack, WhatsApp, your website, your helpdesk or your internal tools - the assistant lives where the work already happens.
We look at the task, the data behind it and what a good answer looks like. Some ideas don't survive this stage, and finding out in week one is the point.
You get: A feasibility read and success criteria
Documents collected, cleaned, chunked and embedded; training data assembled and labelled. This is most of the real work.
You get: A working retrieval or training pipeline
The assistant or model, plus an evaluation set of real questions with known answers. We tune against measured accuracy, not impressions.
You get: A measured accuracy score you approve
Deployed into your channels with logging, cost controls and drift monitoring, so quality is watched rather than assumed.
You get: A live system with a quality dashboard
One well-defined use case, built properly and measured, so you can decide with evidence.
Typical timeline: 4–6 weeks
A system your customers or staff depend on daily, with the monitoring that implies.
Typical timeline: 8–12 weeks
Fine-tuned or bespoke models where a general-purpose API genuinely isn't enough.
Typical timeline: 12+ weeks
Practical AI that earns its keep: retrieval-augmented assistants grounded in your own data, document and workflow automation, and predictive models integrated with the systems you already run.
Retrieval-augmented generation: the assistant is only allowed to answer from documents we've indexed for you, and every answer carries a citation back to its source. It's prompted and tested to say it doesn't know rather than guess. We then build an evaluation set from real questions with known answers and measure accuracy against it before launch and continuously afterwards.
No. We use enterprise API tiers where the provider contractually does not train on your data, and for sensitive workloads we can deploy open models in your own cloud or on-premise so nothing leaves your infrastructure at all. Which route makes sense is one of the first things we agree.
Running costs are usually smaller than people fear - a typical internal assistant handling a few thousand questions a month runs $50–$400 in model costs. We build in caching, cost controls and per-user limits, and show you the projected monthly figure during the pilot so it's never a surprise.
For an assistant, as little as a few dozen documents - it reads rather than learns, so you need coverage, not volume. For predictive models it's different: you generally want at least a year of history and a few thousand examples. We tell you honestly in the feasibility review if there isn't enough, before you've committed to a build.
Whichever fits the task and the constraints - Claude, GPT, Gemini, or open models like Llama and Mistral when data has to stay in your infrastructure. We build the system so the model is swappable, because the landscape moves fast and you shouldn't be re-engineering every time a better or cheaper option appears.
Yes. The models we use handle Urdu, Arabic, Punjabi and most major languages well, including mixed-language questions. We test multilingual accuracy as part of the evaluation set rather than assuming it works.
Then you've spent four to six weeks and a defined amount finding that out, instead of nine months. That's what the pilot is for. We'd rather tell you a use case isn't ready than build something you quietly stop using - and we'll usually be able to point at what would need to change to make it viable.
Yes - that's usually where the value is. We connect to CRMs, ERPs, helpdesks, Slack, WhatsApp, SharePoint and internal databases through their APIs. If a system has no API we'll look at other routes, and tell you plainly when the integration is the expensive part.