Service
AI & ML Workflows
Automation built into your operation, not a demo
Most AI projects fail not because the model is wrong but because nothing changes in the business afterwards. We build AI and machine learning into workflows that people already use — a document that gets read automatically, a forecast that fills a purchase order, an image check that stops a bad part. The measure of success is whether a job that used to take a person now does not.
What we build
Scope of this service
Document processing
Invoices, purchase orders, delivery notes, lab reports and forms read automatically, extracted into fields and pushed into your system with a human review step for anything uncertain.
Demand and stock forecasting
Models trained on your own sales and stock history to drive re-order points, rather than a fixed minimum level set years ago.
Computer vision
Quality inspection, counting, presence and absence checks, damage detection and number plate recognition on a production or logistics line.
Predictive maintenance
Sensor and usage data used to predict failures before they stop the line, and to schedule maintenance from condition rather than from a calendar.
Chat and agent workflows
Assistants that answer from your own documents and data, and agents that carry out multi-step tasks inside your systems with an audit trail.
Anomaly detection
Unusual patterns in stock movement, attendance, transactions or asset use flagged for review instead of found at audit.
Our process
How a project runs
Five stages, in this order. The first one removes most of the risk and is the one clients are most tempted to skip.
- 1
Find the job
We look for a specific, repetitive, measurable task — not a general ambition. "Read 400 supplier invoices a month" is a project. "Use AI" is not.
- 2
Check the data
Whether you have enough data, of good enough quality, is decided before anything is promised. Where you do not, we say so and propose a way to start collecting it.
- 3
Prove it small
A pilot on real data with a measured accuracy figure, run against the current manual result. You see the number before you commit to a rollout.
- 4
Build it in
The model goes into the workflow people already use, with a review step for low-confidence cases so a wrong answer never reaches the system silently.
- 5
Watch it
Accuracy is monitored after go-live and the model is retrained when the data drifts. A model that is not monitored quietly degrades.
Technology we work with
Models
- Large language models (hosted and self-hosted)
- Vision models
- Time-series forecasting
- Classical ML
Tooling
- Python
- PyTorch
- ONNX Runtime
- OpenCV
- Tesseract and modern OCR
Serving
- REST APIs
- Edge inference on-site
- Batch pipelines
- GPU and CPU deployment
Data
- MongoDB
- PostgreSQL
- Object storage
- Vector stores
Examples
Work of this kind we have delivered
Described by what was built rather than by client name, until we have permission to publish.
Invoice extraction
Supplier invoices read and matched against purchase orders, with only exceptions reaching a person.
Vision counting on a line
Camera-based counting and presence checks integrated with RFID reads so the two verify each other.
Stock forecasting
Re-order points driven by demand patterns per item and per location rather than by a single static minimum.
Document assistant
A question-answering assistant over an organisation's own manuals and SOPs, with citations back to the source document.
Engagement models
How we can work together
Which one fits depends mostly on how settled the requirement is, and how much you want to steer week to week.
Pilot
A fixed-price pilot on your real data with a measured accuracy result, before any commitment to a full build.
Build and integrate
The proven pilot built into your workflow and deployed, fixed scope and price.
Managed
Ongoing monitoring, retraining and improvement under an annual contract.
Questions
Common questions
Do we need a lot of data to start?
It depends on the job. Document extraction and vision often work from a few hundred examples. Forecasting needs a reasonable history. We tell you honestly at the data-check stage, before you spend anything on a build.
Can the model run on our own servers?
Yes, for most use cases. Self-hosted models on your infrastructure keep the data inside your building, which matters for healthcare and government work.
What accuracy can you promise?
Nothing before the pilot. After the pilot we give you a measured number on your own data, and you decide whether it is good enough to deploy.
Start a conversation
Tell us about your ai & ml workflows project
Send the problem rather than a specification. We will come back with an approach, an engagement model, and an honest view of whether an existing product would be cheaper.
Tell us what you need to track
Send us the problem and we will come back with a specification — the software, the hardware, and a read-rate trial on your own goods before you order anything.
Or call +91 97305 32888 — Monday to Saturday, 9:30 am to 6:30 pm.