AI and Machine Learning
Practical AI for documents, workflows and reporting, with human review
Practical AI features for business software, including document extraction, language processing and automation with human review.
Who this is for
Many organisations spend staff hours reading forms, re-typing data from scanned documents or searching through files to answer routine questions. We offer AI and machine learning development for institutions and businesses that want to reduce this manual work, starting with narrow, well-defined tasks where the output can be checked and the benefit is clear.
Our approach is cautious by design. We assess whether AI is the right tool before building anything, keep people in the loop to review AI output before it is acted on, and handle personal or confidential data in line with the DPDP Act 2023 and your own policies. Where a simpler rule-based solution works, we will say so.
What we deliver
- AI-powered business applications
- Adding AI features such as classification, summarisation or smart search into new or existing web applications, scoped to a specific task your team performs.
- Intelligent document processing
- Pipelines that sort incoming documents, pull out the fields you need and route them for review, with confidence scores so low-certainty items go to a person.
- OCR and document extraction
- Reading text from scanned forms, certificates, invoices or printed records, including mixed English and Hindi documents, and converting it into structured data.
- Natural language processing
- Applying language models to tasks like categorising feedback, summarising long documents or extracting names, dates and amounts from free text.
- Workflow automation
- Combining AI steps with conventional rules to automate repetitive handoffs, such as triaging requests or pre-filling records, with approval points kept in place.
- AI integrations
- Connecting your applications to AI model APIs or self-hosted models, with controls on what data is sent, logging of requests and fallbacks when a service is unavailable.
- Retrieval-augmented generation (where appropriate)
- Question-answering over your own documents that cites its sources, recommended only where the content is well maintained and answers can be verified.
- AI-assisted reporting and analytics
- Generating draft narrative summaries or highlighting unusual patterns in your data, presented alongside the underlying figures so reviewers can check them.
What changes for your organisation
Less manual data entry
Staff review and correct extracted data instead of typing it in from scratch.
Human review built in
Every design includes a step where a person checks AI output before it affects records or decisions.
Clear data handling
We document what data the system processes, where it is sent and how long it is kept.
Scoped, testable pilots
Starting with one narrow task lets you measure accuracy on your own documents before expanding.
How a ai and machine learning engagement runs
- 1
Use-case assessment
We examine the task, sample data and risks to decide whether AI is suitable or a simpler approach would do.
- 2
Data review
We study representative documents or records, identify sensitive fields and agree on privacy controls.
- 3
Prototype
We build a small working version and measure its accuracy against examples your team has checked.
- 4
Review workflow design
We design the screens and rules through which people approve, correct or reject AI output.
- 5
Integration
We connect the validated solution to your applications with logging and access controls.
- 6
Monitoring
We track error rates and reviewer corrections so the system can be adjusted as documents or needs change.
Related projects
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Questions about ai and machine learning
Will AI make decisions on its own?
We do not recommend that for consequential decisions. Our designs keep a person responsible for reviewing and approving AI output, especially where it affects students, patients, clients or finances.
Where does our data go, and is it used to train models?
That depends on the option chosen. We can use AI services whose terms exclude training on your data, mask sensitive fields before sending, or use self-hosted models where data must stay within your infrastructure. We document the flow so you can assess it against the DPDP Act 2023 and your own policies.
How accurate will document extraction be?
Accuracy depends heavily on scan quality, handwriting, layout variety and language. We measure it on a sample of your real documents during the prototype stage, so you see actual results before deciding to proceed.
What drives the cost of an AI project?
Key factors are the volume and variety of documents, the number of fields or tasks, integration with existing systems, and ongoing model usage charges if a paid API is used. Self-hosting shifts cost from usage fees to server capacity.
What do we need to provide to get started?
A clear description of the task, a set of sample documents or records with personal data handled appropriately, and a staff member who knows the process well enough to judge whether outputs are correct.
Discuss a ai and machine learning project
Share what you have today and what you need. We reply by email, usually with a few questions and a suggested next step.