AI that looks it up. Not makes it up.
We build AI features that work from your own records: assistants that answer from your documents and databases, document reading that ends manual keying, and automated triage with a person in the loop where it matters. The proof is one scroll away — Emmy, the assistant answering on this very site.
The assistant in the corner of this page? We built it. It is the demo.
Where AI earns its keep
Worth building when the input is messy — and the answer can be checked
AI is not a strategy; it is a tool for specific kinds of work. These are the five places we see it pay off in real businesses.
Answers from your own data
Chat and search grounded in retrieval: every reply is looked up in your records, documents or product data before it is spoken — never guessed from the open internet.
01Document reading
OCR and handwriting recognition that pull structured fields out of scans, forms and invoices — ending the hours staff lose keying the same data in by hand.
02Routing and triage
Models that read free-text messages and send each one to the right team, queue or next action — with the reasoning logged where a human can audit it.
03Drafting inside your workflow
Email replies, summaries and first drafts a person reviews and approves before anything goes out the door. The AI writes; your team stays the author.
04AI as the product
Generative features a product is built around — assistants, agents and recommendations, each with hard limits on what it may change without a human.
05Platform story · Emmy, our own assistant
The AI answering on this site right now
Every claim on this page runs in production one scroll away. Emmy — the assistant in the corner of this website — is our own AI build: grounded in a knowledge base we train, speaking the visitor’s language, and handing over to a human the moment a real conversation matters. We did not build it as a demo; we built it because our own team was answering the same ten questions every day.
The problem
Visitors arrive at midnight with the same questions our team answers every morning — and many of them write in Roman Urdu, which most assistants fumble. Meanwhile a generic chatbot with a blank prompt invents answers about your business confidently, and one hallucinated price is worse than no answer at all.
So the brief for our own assistant was simple: it may only say what it can look up, it must speak the visitor’s language, and it must know when a human has to take over — then get out of the way.
What we built
Grounded in trained knowledge
The assistant answers from a curated knowledge base our team maintains — trained Q&A treated as ground truth, not the model’s imagination.
Answers in the visitor’s language
English, Roman Urdu or Urdu — it replies in whichever language the visitor uses, because our clients write the way they speak.
Human handoff, detected not hoped for
The moment someone asks for a real person — in any phrasing — the conversation moves to our team’s live inbox with the full history attached.
No lead left in the queue
When agents are busy, the assistant captures an email and saves the whole conversation, so the next session resumes exactly where it stopped.
Tested before it goes live
Prompt changes and new knowledge entries are trialled in an admin playground first — the version visitors meet is always the tested one.
Where it stands
Emmy answers around the clock, hands the tricky ones to humans, and has never once invented a price — because it is not allowed to guess. That is the standard we bring to your AI work: connect the model to your own data, and make it look things up instead of making things up.
Want an assistant like this on your site?From pilot to production
Start with one task. Prove it. Then widen.
Pick one task
We start where the input is messy and a wrong answer can be caught — and agree up front how success will be measured.
Ground it in your data
The model gets connected to your documents and databases, then tested on real examples before anyone is asked to rely on it.
Keep a human in the loop
In production we track accuracy and failures, and route the costly decisions to a person — automation widens only where the answers hold up.
Technology
The stack behind grounded AI
The model is chosen per task — accuracy, cost and where your data is allowed to go decide, not fashion.
Models and AI
Core and data
Interface and scale
Decide early
Six decisions that make or break an AI feature
Settle these before the first prompt is written, and the feature stays safe to change for years.
Where the answers come from
Retrieval from your own data, or a model guessing from the open internet? The first design decision is what the AI is allowed to read.
What it may never do
Every agent we ship has hard limits on what it can change or promise — and the steps that matter wait for a person to approve them.
How accuracy gets measured
If a wrong answer cannot be caught, it cannot be fixed. We agree the scoring before the feature ships — not after the first complaint.
Where your data is allowed to go
Which providers may see it, whether it stays inside your own cloud account and who can access what — settled before the first prompt is written.
The update path
Knowledge and prompts change weekly. We build so your team can update the AI without a redeploy — or a developer on standby.
The human escape hatch
Every feature we build knows when to step aside and hand a live human the conversation. That is what makes AI safe to put in front of customers.
AI in action
Running in production — not in a slide deck
Every claim on this page is backed by something you can click. Start with the assistant answering at the bottom of this page.
Related services
Projects like this usually involve
Good to know
Frequently asked questions
Have a task AI could take on?
Tell us what the input looks like and what a good answer is. We reply within one business day — and we will tell you honestly if AI is not the answer.
Prefer to start smaller? Ask Emmy in the corner of this page — it is our work, live.