Sales and orders
- Orders read from email and WhatsApp into the ERP
- Quotations drafted from your price list and past quotes
- Dealer questions on stock and schemes answered instantly
AI and data
AI that does real work in your business
Not a demo, not a chatbot for its own sake. We build AI that reads your documents, answers your customers, forecasts your demand, and takes on the repetitive work, on your own data, with a person in charge of what matters.
What we build
Six ways AI earns its place
Each of these is a standalone service, and most projects combine two or three of them with the software and integrations around them.
- Generative AI and LLM appsPut large language models to work on your own documents and data.Learn more
- AI agents and workflow automationSoftware that reads, decides, and acts across your systems, with a person in charge.Learn more
- Document processing and OCRInvoices, purchase orders, and forms read automatically into your systems.Learn more
- AI assistants and chatbotsAnswer customers on your website and WhatsApp, day and night, from your own information.Learn more
- Forecasting and data analyticsUse the data you already collect to see what is coming, not only what happened.Learn more
- Computer visionCameras that inspect, count, and read, on the shop floor and in the field.Learn more
Use cases
Where it helps, team by team
The best first AI project is usually a task someone does every day and nobody enjoys. A few of the ones we see most:
Finance and accounts
- Supplier bills extracted and matched to purchase orders
- Bank and ledger entries reconciled automatically
- Payment reminders written and sent per customer
Operations and shop floor
- Visual inspection of parts on the line
- Raw-material reorder levels from demand forecasts
- Early warnings from machine sensor data
Customer support
- Website and WhatsApp assistants in Gujarati, Hindi, and English
- Tickets sorted and first replies drafted
- Order and invoice status without a phone call
HR and administration
- Policy and process questions answered from your handbooks
- CVs screened against the role, with reasons shown
- Meeting notes and reports summarised
Management
- Plain-language questions answered from your data
- Weekly summaries of what changed and why
- Customers and products flagged when trends turn
How we deliver
Prove it small, then build it properly
AI projects fail when they start big and vague. Ours start with one workflow and a number to beat.
- 01 · Week 1
Pick one workflow
We find a single, repetitive task where AI can save measurable time or money, and record how it performs today.
- 02 · Weeks 2 to 4
Prove it on your data
A working prototype on a sample of your real documents or conversations, with measured accuracy and cost per task.
- 03 · Weeks 5 to 10
Build it properly
Integration with your systems, review screens, permissions, logging, and fallbacks for when the model is unsure.
- 04 · Ongoing
Measure and improve
Monthly reports on accuracy, time saved, and running cost, with the model and prompts tuned as your data grows.
Responsible AI
The rules we build by
AI is only useful if people can trust it with real work. These four rules apply to every AI system we ship.
- Your data stays yours
- We use enterprise AI services that do not train on your data, or run open models inside your own cloud when data must not leave it.
- A person stays in charge
- Anything that touches money, stock, or customers waits for approval until the system has earned trust, and every action is logged.
- Measured, not guessed
- Every AI feature is tested against a set of real examples with known answers, before launch and after every change.
- Running cost under control
- We match the model to the task, cache what repeats, and report the cost per task, so the monthly bill holds no surprises.
AI in our own work
We use AI to build your software, too
AI tools make our engineers faster at the routine parts of the job, which leaves more time for the parts that need judgement. The responsibility for every line still sits with a person.
- AI coding assistants for boilerplate, refactoring, and documentation, with every change reviewed by an engineer before it is merged
- Test cases and edge cases drafted with AI from the requirements, then checked and kept by a tester
- AI-assisted code review on every pull request, as a second pair of eyes alongside the human reviewer
- Faster discovery of old systems by having AI read through legacy code, scripts, and logs
- Client code is only shared with AI tools approved for the project, and never with consumer chat apps
Technology
Models and tools we work with
We are not tied to one AI vendor. Models improve every few months, so we design systems that can switch to a better or cheaper one without a rebuild.
AI models and platforms
Commercial and open models, chosen per task, through enterprise APIs or in your own cloud.
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- Azure OpenAI
- AWS Bedrock
- Vertex AI
- Ollama
AI frameworks and data
The plumbing behind assistants, agents, document processing, forecasting, and vision.
- LangChain
- LangGraph
- LlamaIndex
- Model Context Protocol
- pgvector
- Qdrant
- Hugging Face
- PyTorch
- scikit-learn
- OpenCV
- n8n
Questions
AI questions we hear often
We are a small business. Is AI worth it for us?
Often, yes, if there is a repetitive task with a lot of reading or typing: orders, bills, customer questions. We start with one workflow and measure the time saved before you invest further.
How much does an AI project cost?
A proof of concept on your data is a small, fixed-price piece of work. If it proves its value, the full build is quoted separately with a fixed price, and the monthly running cost is estimated upfront.
Will our data be safe?
Yes. We use enterprise AI services that exclude your data from training, restrict access by role, mask sensitive fields where needed, and can run models inside your own cloud.
What if the AI makes a mistake?
We plan for it. The system shows its sources and confidence, sends uncertain cases to a person, and logs every action so it can be checked and reversed.
Which AI models do you use?
Whichever fits the task and budget: OpenAI, Anthropic Claude, Google Gemini, or open models such as Llama and Mistral. We test more than one on your data before choosing.
Can you add AI to software we already have?
Yes. Most of our AI work plugs into existing ERPs, CRMs, Tally, websites, and WhatsApp, rather than replacing them.
Does AI work in Gujarati and Hindi?
Yes. Current models handle Gujarati, Hindi, and English well, including mixed-language messages. We test on your real conversations to confirm.
Do we need a lot of data to start?
Not for assistants and document processing, which work from your existing documents. Forecasting and vision need history or example images, and we check what you have first.
Free AI consultation
Tell us about the task you want off your plate
Describe the work, how often it happens, and what it costs you today. We reply within one working day with an honest view on whether AI is the right fix.
- No obligation, no sales pressure
- A small, fixed-price proof on your own data
- A straight answer if AI is not the right tool