Automate the routine. Keep the judgement.
AI & automation in Indore
Replace manual data entry, disconnected app transfers and delayed customer handoffs with custom-built AI agents that run the same way every time, around the clock.
What this is costing you right now
Records are always behind
Cross-department systems updated by hand, so every record is stale for most of the working day.
Copy-paste errors pile up
Copy-paste between invoicing, CRM and order tracking introduces errors that surface weeks later in reconciliation.
More orders need more staff
Headcount scales with transaction volume instead of software absorbing it.
What gets delivered
| Component | Technical Execution | Client Benefit | Output |
|---|---|---|---|
| Custom Agent Development | Retrieval-augmented or fine-tuned agents scoped to one domain task, with evaluation sets per workflow. | Behaviour you can test, not a chatbot you have to trust. | Deployed agent + eval suite |
| Document Processing (OCR + LLM) | Extraction and structured insertion from PDFs, invoices, purchase orders and email bodies into your database. | Paper and email stop being a data entry queue. | Parser service + schema |
| Multi-Step Orchestration | Conditional, retrying workflows on n8n, Make, or Python microservices with idempotent steps. | A failed third-party call retries instead of dropping the record. | Orchestration graph + runbook |
| Human-in-the-Loop Triggers | Exception routing to Slack, WhatsApp or Teams whenever a configurable confidence threshold is not met. | Automation never silently guesses on the edge cases. | HITL queue + escalation policy |
Architecture & Tools
Orchestration & Logic
- Python
- LangChain / LlamaIndex
- n8n
- Supabase
Model Layer
- Current-generation frontier models from Anthropic and OpenAI, selected per workload latency requirement
- Speech-to-text
- Vision extraction
Memory & Retrieval
- Pinecone
- Qdrant
- pgvector
Deployment Options
- Managed cloud
- Local inference via Ollama / vLLM where data cannot leave premises
- Client data goes only to model providers’ business APIs, which do not train on it by default. Zero-data-retention terms are arranged where a workload needs them and the provider allows it.
- End-to-end encryption in transit and at rest; credentials held in a managed secret store, never in code.
Who this is for
Good fit
- Businesses with 10+ staff processing 100+ repetitive entries, tickets or documents a day
- Teams already running two or more systems that do not talk to each other
- Operations where a wrong record costs real money downstream
Not a fit
- Solo operators looking for ChatGPT prompts or an off-the-shelf wrapper
- Teams without a documented process to automate — we would be automating chaos
- Anyone needing it live next week
One example from our work.
A real engagement, client name withheld under NDA.
Measured in the engagement
India
Tier-1 precision tooling & discrete engineering manufacturer
- Invoice turnaround
- 7–10 days to 1–2 days
- Back-office data entry
- ≈60% less
- Reconciliation errors
- ≈12% to under 3%
The problem and what was built
The problem
Work was split between a legacy Tally ERP 9, spreadsheets nobody had standardised, and WhatsApp threads. Vendor invoices took seven to ten days to clear, and three-way matching of purchase orders, goods receipts and invoices carried an estimated 12% clerical error rate.
What was built
- Autonomous ingestion workers capturing vendor invoices from email and WhatsApp
- OCR and LLM field extraction mapped strictly to Tally ERP 9 ledger schemas
- A deterministic three-way reconciliation engine that routes its own discrepancies
- Idempotent RPA writeback into Tally ERP 9, so a record cannot post twice
- A finance exception dashboard for what the engine will not decide alone
What should you
fix first?
Tell us how your business works today. We’ll help you identify the first problem worth solving.
Five questions, about a minute
Free · 30 minutes · on WhatsApp with Aadil