About Tech4Biz
Serious systems, understood all the way down.
Tech4Biz is an engineering firm for high-stakes AI, software, cloud and hardware. We were founded in 2016 in Bengaluru, and we deliver for clients in the US, Europe and India.
Scroll down the stack, from cloud to silicon
What we believe
Nothing refuses to be understood if you pay it enough attention.
Problems rarely stay inside one layer. A fault that looks like software can turn out to be firmware timing, and a model that looks wrong can turn out to be reading the wrong rule. We follow a problem down through every layer it touches until we understand it, and everything else about this company follows from that habit.
The review
Bring the questions your review panel would ask.
Pick one. Each answer comes from work we delivered, and the stack shows which layers that work went through.
Where does the model’s judgment stop?
We use the model for drafting and code for decisions, because only code can be relied on when a decision is checked later. On the control-assurance platform we built for a bank, the model reads the evidence and a deterministic engine decides, which leaves none of the decisions on validity, ownership or reliance to the model. Every run is logged so it can be replayed and audited. The platform has 12 services and its own workspace for each of the 3 lines of defense, and it reached a working proof of concept in 5 sprints.
0decisions on validity, ownership or reliance left to the model
Read the projectThis work went through these layers: Applications, AI models.Do you actually go below the software?
Yes, down to the board. We designed the entire hardware for a dashcam on an automotive vision SoC, designed and delivered an FPGA board, and design PCBs and deliver boards for customers. For a commercial vehicle we engineered a CAN FD to Ethernet gateway with a security module, with OTA-capable firmware on FreeRTOS running on a dual-core ARM R5F microcontroller, secure boot with a hardware security module, 2 automotive Ethernet ports and 8 CAN and CAN FD channels.
8CAN and CAN FD channels on one commercial-vehicle gateway
See hardware engineeringThis work went through these layers: Firmware, Boards.How do you know migrated code still does what the old code did?
Code that compiles after translation does not show that it still behaves the same. Our verifier generates regression tests for the translated code and flags every place where it cannot show that the behavior matches, instead of passing it quietly. Across 14 customer COBOL systems of about 0.2 million lines, it generated and ran 8,700 regression tests and flagged 412 semantic mismatches.
412semantic mismatches flagged across 14 COBOL systems
Read the write-upThis work went through these layers: Applications, AI models.Can AI run where there is no cloud?
It can, if the model is fitted to the hardware. On an FPGA, a language model is held back by memory as much as by compute, which is why our 3 case studies cover 4-bit inference, memory bandwidth and fabric offload. We have built 4 solutions on one FPGA module, from an in-vehicle assistant to a field service copilot.
4solutions built on one FPGA module
See hardware engineeringThis work went through these layers: AI models, Boards, Silicon.Can deterministic rules really carry medical claims?
A claim is decided on the detail of each insurer’s policy, and a model that is usually right about that detail still sends some claims out wrong. So we encoded the policies as rules and check every claim against the insurer’s own rules before it goes. The rules came from 701 canonical cases from major US insurers, and they add up to about 1,250 deterministic rules across 4 major insurers’ policy sets.
701canonical cases from major US insurers
Read the projectThis work went through these layers: Applications, AI models.Can you deliver at institutional scale?
We are in delivery on a private cloud program for an engineering institution, which runs 22 labs from a browser on hardware the institution had already bought, rolled out over three years with 14 labs in the first two. The bulk storage tier holds 109 TB usable, and on fixed memory the platform guarantees about 280 concurrent sessions at the busiest lab mix and runs about 450 in practice.
22labs over three years, 14 of them in the first two
See private cloudThis work went through these layers: Cloud, Platform.How fast can you move when the work is regulated?
In GxP work, speed only counts if every document version stays provable and every transition is enforced. The quality management platform we built for a Tier 1 enterprise was held to that standard, and its first backend was running 7 days in and its main portal 2 weeks from the start, across 22+ phases of work.
7 daysto the first backend on a GxP quality platform
Read the projectThis work went through these layers: Platform, Applications.What can your AI assistant actually do in our systems?
The enterprise assistant we built answers from live data through a query service with 11 actions, and every one of them is read-only, so it can look things up there without changing anything. That gives exact answers beyond what search can give, with the data refreshed every 2 hours against a requirement of under 12, and the same assistant searches 17,295 wiki documents.
11read-only actions in the query service
See AI engineeringThis work went through these layers: Platform, AI models.Chapter 01
The machine we never sold
In 2020 the world stopped, and India needed oxygen concentrators. We bought the best machine we could get, took it apart, scanned it in 3D and studied it down to the physics. Then we built our own, with the sieve beds designed and molded for manufacture rather than improvised, and every part chosen for production. The unit came out compact and fit for production, and we never put it on sale.
Chapter 02
The cloud nobody sensible would build
Years later we wanted to know whether we really understood cloud infrastructure, or had only deployed onto other people’s. A test cluster could not have answered that, so we took space in a Tier‑4 data center in India, bought the class of servers the major clouds run on, and architected the platform the way a public cloud is architected, with our own control plane engineered and hardened in-house. Cloudtopiaa runs customer workloads there today.
Chapter 03
The work without our name on it
Much of our work carries other firms’ names. Larger companies bring us the piece that has to actually work: the architecture under a platform that cannot fail in public, the silicon-level product a roadmap could not free the depth for, the proposal that had to exist by morning. It ships under their brand, the NDA holds, and this paragraph is as specific as we will ever get.
Published
Check our work before you call us.
We have published two things you can judge on your own time, without a sales call in the way.
Determ8
The gate pipeline for AI agents. Its core is open source under the Apache 2.0 license, with four free gates: cheap gates run first and expensive ones last, and the first failure stops the run.github.com/Tech4Biz-Solutions-Pvt-Ltd/-Tech4Biz-Determ8
Read the codeVerifying AI-migrated code
How our verifier checks COBOL translated to Java and flags what it cannot check, with the method laid out.tech4bizsolutions.com/insights
Read the write-upPrinciples
What we hold ourselves to.
Provable
Decisions that matter should be provable, not just probable.
Honest
Recommendations come with reasons and costs, including the recommendation not to hire us.
Published
Our tools and benchmarks are public, because credibility should be checkable.
Durable
We engineer for the year the system is alone in production, not the week of the demo.
Layer 07 / Silicon
Your work gets the same treatment: the depth goes in, and your name stays on it.
If your problem needs someone who will not stop before the bottom, we would like to hear about it.
Talk to our engineers