Assistants on your own content
A question-answering layer over the documents, tickets and records you already hold — grounded in them, citing them, and admitting when the answer is not there.
AI development services
AI that cites its source — and admits when it does not know.
Assistants grounded in your own documents, paperwork that reads itself, agents that finish the job inside your systems, and vision on the line. We integrate the best model available and spend the budget where trust actually comes from: retrieval, verification, evaluation and a human checkpoint where being wrong would cost something.
- Every answer cites its source
- Claude · GPT · Gemini, swappable
- Runs on-prem or air-gapped
- Graded pilot in ~4 weeks
Live demo · grounded answering
Ask it something it cannot answer.
A sample field-service handbook, searchable. The three suggested questions are answerable; the fourth is deliberately not, because refusing cleanly is the behaviour that decides whether a system like this can be put in front of customers. The ranking is computed in your browser as you type.
Retrieval running in your browser8 passages · field-service handbook
Grounded
24 months from the date of despatch, covering manufacturing defects and component failure under normal use. Water ingress above IP54, unauthorised firmware and impact damage are excluded. Warranty terms p.4
“Every unit carries a 24-month warranty from the date of despatch. The warranty covers manufacturing defects and component failure under normal operating conditions. It does not cover damage from water ingress above IP54, unauthorised firmware, or physical impact.”
Retrieval trace
WR-04Warranty terms51%
warranti
- confidence gate
- 12%
- top score
- 51%
- verdict
- answer
The ranking above is computed live — TF-IDF over the sample passages, scored against what you typed. The wording of each answer is pre-written: this page does not call a model. In a real build that last step is the LLM, and everything else on this panel is what keeps it accountable.
0%
Of answers cite the passage they came from, or the system says it does not know.
0 weeks
From brief to a working pilot on your own documents, evaluated against a real answer set.
0 models
Claude, GPT and Gemini behind one interface, so a price change is a config change.
0
Vendor lock-in. The prompts, the evaluation set and the pipeline are yours at handover.
What we build
Applied AI that decides, not demos that impress.
Everything here has a measurable job: fewer hours on a task, fewer errors reaching a customer, or an answer available at two in the morning that previously needed a person.
Document intelligence
Invoices, POs, lab reports, contracts and scanned forms turned into structured fields with a confidence score per field and a human queue for the ones that need a look.
Agents that take actions
Not a chat window — a process. Read the input, decide, call the systems that do the work, and stop for a person at the point where being wrong would cost something.
Computer vision
Defect detection on a line, counting, presence and measurement, OCR of plates and labels — running on a server or on the device itself where latency or privacy demands it.
Forecasting & scoring
Demand, churn, credit and maintenance models built on your history, with the feature set written down and the accuracy reported honestly against a holdout.
Voice & multilingual
Speech to text and back in Indian languages, for support lines, field reporting and anything where typing is the reason a system goes unused.
Evaluation & guardrails
A graded answer set from day one, regression runs on every prompt change, PII redaction, injection defence and a log of what the model was actually asked.
On-device & private AI
Small models running on an edge box or a phone, air-gapped where the data cannot leave the building — the same stack we ship in our own hardware work.
How it is built
The model is one box out of five.
Most of what makes an AI feature trustworthy happens either side of the model. That is the part we are actually selling, and the part that survives you changing provider next year.
01
Ingest
PDFs · scans · tickets · database · e-mail
Parsed, chunked, versioned
02
Retrieve
Vector + keyword hybrid · filters · rerank
The passage, not the vibe
03
Reason
Claude · GPT · Gemini · local models
One interface, swappable
04
Verify
Citations checked · schema · PII · evals
Refuses rather than invents
05
Act
Your ERP, CRM, ticketing, WhatsApp
Human approval where it matters
We do not train a foundation model, and we will tell you when fine-tuning is the wrong answer — which it usually is. Retrieval, evaluation and a sensible human checkpoint beat a fine-tune on almost every business problem we are handed.
Where it earns its keep
Six places AI has paid for itself in our work.
Chosen because each has a number attached — hours, error rate or response time — not because it demos well on a stage.
The answer desk your team stops interrupting
Field engineers and agents ask in plain language and get the clause, the torque figure or the RMA rule with the page it came from. The questions it cannot answer become the list of documents worth writing.
Paperwork that reads itself
Invoices, delivery notes and POs matched three ways, with the exceptions — and only the exceptions — arriving in a human queue with the reason attached.
The inspection that happens every time
Vision on the line catching the defect class that gets through at 3 am, running on an edge box so the decision is made in milliseconds and nothing leaves the factory.
Notes, next steps and nothing retyped
Calls summarised, CRM fields filled, follow-ups drafted in the rep's own voice — reviewed in one click rather than written from scratch at the end of the day.
Search that respects the source
Long, dense, consequential documents made searchable with citations and an audit log of who asked what, so a wrong answer can be traced rather than argued about.
The feature that makes the product feel modern
Onboarding that understands a photo, a recommendation that explains itself, a chat that knows your catalogue — shipped inside an app we can also build.
What we will not claim
The four sentences most AI pages leave out.
Every one of these has cost us work. They have also meant we have never had to walk a client back from a promise.
We do not train foundation models
Nor should you pay anyone to, for a business problem. We integrate the best available model and spend the budget on retrieval, evaluation and the workflow around it.
Accuracy is quoted against your data
Any number we give you comes from a graded set built on your documents during the pilot. Benchmarks from a vendor's blog post are not evidence about your corpus.
Some problems are not AI problems
If a lookup table, a validation rule or a better form solves it, that is what we will recommend — and it will be cheaper and more reliable than anything with a model in it.
A human stays in the loop where it counts
Anything that moves money, contacts a customer under your name or touches a clinical or legal record goes through an approval step until measured accuracy says otherwise.
How we start
A pilot on your own documents in about a month.
Fixed price for the pilot, measured against an answer set your team grades. If it does not clear the bar, you have spent a month finding that out rather than a year.
- 013–5 days
Feasibility read
We take one real process, look at the data behind it and say whether AI is the right tool. Roughly a third of the time the honest answer is a rule, a form or a database index — and we will say so before you spend.
- 023–4 weeks
Grounded pilot
One workflow, your documents, a graded answer set built with the people who do the job today. Fixed price, and a number at the end you can hold us to.
- 034–8 weeks
Production
Integrations, access control, PII handling, audit logging, cost caps per tenant and the human-review queue. This is the part that takes the time, and the part that decides whether anyone still uses it in month six.
- 04monthly
Run & improve
Regression runs on every prompt change, model upgrades as they land, and a monthly report of what it answered, what it refused and what it cost.
FAQ
What people ask before signing.
If yours is not here, send it with your brief — an engineer replies within one working day.
No. We use the enterprise or API tiers where the provider contractually does not train on your inputs, and for anything sensitive we run open-weight models on infrastructure you control — including fully offline. Which of the three applies is decided in week one, in writing.
Tell us what the product has to do.
We'll tell you what it takes.
Startups, enterprises and product teams — from a napkin sketch to a fleet in the field. One team, concept to production.
- Udyam UDYAM-UP-50-0280551
- GSTIN 09CPBPD9064H1ZS
- Lucknow, Uttar Pradesh
- Source code and IP yours
- Reply in 1 working day
Start a project
Come with a problem. We'll scope it before we quote it.
Send us the constraint that worries you most. An engineer reads it — not a form queue — and replies within one working day.
Direct
- Emailadmin@thegradlew.com
- Phone+91 79859 20361 · Sourabh
- Websitethegradlew.com
- Book a callcalendly.com/abhishek-thegradlew
- OfficeKN135MINB, Neel Matha, Malak Road, Lucknow, Uttar Pradesh 226002, India
Lucknow, Uttar Pradesh · India
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