Good AI looks effortless. That is because all the difficulty was absorbed before it ever reached the user.
Author
Founder, Espial Infotech
Espial Infotech
Building AI that works
We build human-centred AI systems that change how work gets done — on factory floors, in energy grids, inside sales teams, and in our own products.
About Us
Research-Led AI,
Built for Production
Built for Production
Our Mission
Based in Pune, India, we build AI for the real world. Most intelligent systems die between the notebook and the factory floor — the demo works, the deployment does not. Our mission is to close that gap: to unite applied research, product design and hard engineering so that the AI we ship is practical, trustworthy and still running a year later. We hold ourselves to it in the most direct way possible — by building and operating our own AI products, LinkSprig and PetalRank, on the same standards we sell.
Core Values
Guided by Principles
Rigour
We do not guess. Every architectural decision is grounded in benchmarks, evaluations and evidence, and every claim we make about a system is one we can reproduce on demand. Research discipline is not academic overhead — it is what separates a model that works in a notebook from one that holds its accuracy on a production line for two years. We measure first, build second, and we publish the numbers internally whether they flatter us or not.
Ownership
We own the hard part of deployment so our clients do not have to. That means our engineers are on site aligning optics, tuning sensors and integrating with PLCs — not filing a ticket and waiting. When something breaks at 2am, there is one accountable loop, and it runs back to the people who wrote the code. Ownership is not a value we put on a wall; it is a support commitment.
Craft
AI is only as good as the experience wrapped around it. We care about the interface, the error state, the moment the system admits it does not know. Craft is the difference between an assistant people route around and one they reach for. We prototype, we test with real users, and we keep refining until the smart thing to do is also the obvious thing to do.
Honesty
We will tell you when AI is the wrong answer. We will tell you when a model is not accurate enough to ship, when a number is not defensible, and when a deadline is not real. It costs us work occasionally. It has never cost us a client.
FAQs
Frequently
asked questions
asked questions
A focused pilot reaches a working, testable v1 in four to six weeks. A production system - with on-site deployment and integration-typically runs eight to twelve weeks depending on hardware, data access and how many systems it has to talk to. We tell you which one you are in during discovery, not after.
A clear problem statement, a definition of success, access to sample data, and one stakeholder who can make decisions. That is genuinely it. We run a kickoff workshop to pin down scope and the KPI model before anyone writes code.
Whichever ones wins on accuracy, latency and cost for your problem. In practice: Claude and GPT for reasoning and generation; Llama and Mistral when it has to be self-hosted; YOLO, OpenCV and InsightFace for vision; PyTorch, ONNX and TensorRT for anything on the edge. We are not loyal to a vendor. We are loyal to the benchmark
Yes, and several of our systems do. We have shipped fully on-premise vision analytics that runs a single executable with no internet connection, and self-hosted voice assistants where every inference endpoint stays inside the customer VM. If compliance rules out third-party APIs, we design for that from the start.
Development is included in the project price. Model and API usage is billed at cost, based on your actual volume. We estimate it up front and then work to bring it down - compression, caching and smaller models where a smaller model is enough.
Monitoring, tuning and one support loop that runs from the engineers who built it. AI system drift-data changes, storefronts change, We watch for it and we fix it.
Contact
Let's build
something that ships
something that ships
Tell us the problem. If AI is the wrong tool for it, we will say so.