SERVICES / AI DEVELOPMENT
AI development and automation that fits your workflow.
Copilots, document processing, risk checks and operations automation, scoped at a fixed cost and built on open source with humans in the loop.
(01) What you get
AI that does a real job.
Use-case scoping
We pick the workflow where AI saves measurable effort, and agree what good output looks like before building.
Copilots and assistants
Chat and search interfaces grounded in your own documents and data, with sources shown.
Document processing
Extraction from invoices, forms and contracts into structured data, with a review screen for exceptions.
Workflow automation
Triage, routing and follow-ups wired into the tools your team already uses.
Evaluation and guardrails
Test sets, accuracy checks and fallbacks so you can see when the model is wrong.
Integration and rollout
APIs and interfaces that put the result inside your product, plus handover and monitoring.
(02) Timeline
From pilot to production.
(03) Who it is for
Who gets value from AI first.
Operations teams buried in documents
Invoices, applications and forms that someone retypes every day are a good first candidate.
Fintech and compliance teams
Risk and KYC support with an audit trail, where a person always makes the final call.
Product teams adding AI features
Search, summaries or assistants inside an existing product, built to fit your architecture.
(04) Foundation
Open source where it counts.
We choose models and tooling to suit the task, privacy needs and budget, and keep orchestration code in your repository so you are not tied to one provider.
- Python
- Node.js
- PostgreSQL
- pgvector
- Supabase
- Keycloak
- Docker
- Kubernetes
- AWS
- Azure
- Stripe
- Odoo
(05) Pricing
Pilot first, scale on evidence.
AI PROJECT
Fixed quote
scope, price and date in one proposal
- +Use-case and data review
- +Working prototype with evaluation
- +Integration into your product
- +Guardrails and human review
- +Launch and monitoring handover
We also use AI in our own delivery: it speeds up scaffolding, tests and documentation, and engineers review every line.
(06) FAQ
AI questions, answered.
What is a good first AI project?
A repetitive, document-heavy or search-heavy task where the current cost is clear and a person can check the result. Those projects are easiest to scope at a fixed cost.
Will our data be used to train public models?
We design for your privacy requirements. That can mean providers with no-training terms or self-hosted open models. We confirm the approach in the proposal.
How do you deal with wrong answers?
We build test sets, show sources, set confidence thresholds and route doubtful cases to a person. No model is perfect, so the workflow plans for mistakes.
Can you add AI to our existing product?
Yes. We add it as a service behind your current architecture, so it can be improved or swapped without a rewrite.
Do you build fintech AI such as risk or invoice capture?
Yes. PaisaPravaah, our invoice management product, uses AI-assisted data capture, and we design fintech features with audit trails.
Who owns the models and code?
You own the code, prompts, evaluation sets and infrastructure we build. Third-party model terms are listed in the proposal.
How do you measure whether an AI feature is working?
We agree a small set of real examples and success criteria before building, then check results against them as we go. After launch we log inputs and outputs, with privacy controls, so quality can be reviewed.
What does an AI feature cost to run?
Model usage is billed per request or per volume, so we estimate it from your expected usage and show the figure in the proposal. We design caching and fallbacks to keep it predictable.
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