AI Solutions
The work nobody should be doing by hand.
Document processing, data entry and approvals automated into your existing systems, with humans kept where judgement matters.
Sound familiar?
- “Three people spend their day moving data between systems that don't talk.”
- “Invoices and forms arrive as PDFs and someone types them in.”
- “Your RPA bots break every time a supplier changes their invoice layout.”
Capabilities
What it does.
Document extraction
Invoices, POs, forms and contracts read and validated regardless of layout.
Handles variation
Unlike rule-based RPA, it copes with different formats and wording.
System integration
Writes into your ERP, CRM, accounting and databases directly.
Human-in-the-loop
Confidence thresholds send uncertain cases to a person for approval.
Full audit trail
Every extraction, decision and write recorded and reviewable.
Cost instrumented
Per-document cost visible before you scale it across the business.
How we deploy it
The path from pilot to production.
Most AI projects die between a good demo and a working deployment. This is the sequence that avoids that.
Pick one workflow
Highest volume, lowest judgement. Programmes that start with a strategy deck rarely reach production.
Baseline it
Measure the current time and error rate so improvement is provable rather than asserted.
Build with a fallback
The failure path is designed first: uncertain cases go to a person, never through silently.
Pilot in parallel
Runs alongside the manual process until accuracy is demonstrated.
Expand
Next workflow once the first is measurably paying for itself.
Data & privacy
Where your data goes, plainly.
This is the first thing enterprise procurement asks about AI, and most vendor pages avoid answering it.
- Documents processed through enterprise tiers that exclude your data from training.
- Sensitive fields can be masked before the model sees them.
- Processing can run entirely in your own environment with open-weight models.
- Retention configurable — documents can be deleted immediately after extraction.
What it costs to run
Document processing typically costs ₹1–5 per document. Against a manual baseline of two to four minutes of staff time, payback is usually measured in weeks rather than quarters.
Technology
What we build it with.
- OpenAI
- Anthropic Claude
- Python
- LangChain
- n8n
- Node.js
- PostgreSQL
- AWS
We're not tied to a model vendor. Systems are built so a provider can be swapped as capability improves or prices move.
Industries
Where it applies.
Traditional RPA follows fixed rules against fixed screens and breaks when anything changes. AI-based automation handles variation — different invoice layouts, differently worded emails — which is where most real processes actually live.
For structured document extraction, 92–98% field accuracy is typical after tuning. The important number isn't accuracy though — it's what happens to the remaining few percent, which is why human review on low-confidence cases is non-negotiable.
The highest-volume, lowest-judgement task you do. Usually document processing or moving data between two systems. One workflow, measured, then expand.
Anything touching money or compliance keeps an approval step. Low-confidence extractions route to a person. The system is designed so a mistake is caught, not so mistakes never happen.
Start here
Tell us the process, not the technology.
The best AI projects start from an expensive manual task, not from a decision to use AI. Describe the task and we'll tell you honestly whether this is the right tool.