TensorIoT Start a pilot
AI & Machine Learning Services · Est. 2017 · US / India / Japan SHEET 01

AI, applied.
Not demonstrated.

We design, build, and operate production AI for enterprises that need the model to survive contact with the real world — the ward, the plant floor, the trading desk, the back office. Running in production since 2017.

Start a pilot → See the portfolio →
Founded
2017building production AI ever since
Customers
300+enterprises across industries
Countries
03United States · India · Japan
Cloud platforms
03AWS · GCP · Azure certified teams
Why now — the case for applied AI 02

Most enterprise work is
language and pictures

For fifty years the majority of what an organisation writes, files, photographs, and reads sat beyond the reach of software, so people carried it by hand. That is the part that changed — and it is the only reason any of this is worth doing now.

The constraint was never ambition. It was that software could not read a handwritten note, watch a production line, or draft a defensible document — so the most expensive people in the building did it instead.
  • Language. Models now read and write the notes, letters, claims, and reports that stayed manual for decades.
  • Vision. Cameras you already own become measuring instruments — defects, counts, compliance, safety.
  • Evidence. The first wave of enterprise deployments has published results. A programme can start from evidence, not experiments.
  • Governance. Requirements are defined and auditable. Build for them from sheet one rather than patching before launch.
01

Read

Vision-language models decipher handwriting, forms, and scans — and route anything uncertain to a person rather than guessing.

02

Watch

Edge inference turns existing camera estates into continuous measurement, offline-first and without new hardware.

03

Predict

Forecasting and anomaly models put demand, failure, and risk hours or weeks ahead of the people who act on them.

04

Act

Agents take the routine end-to-end — with grounding, permissions, and a named human gate on anything consequential.

Watch None of the value shows up on a slide. It shows up when the system runs on your data, in your environment, against a number you agreed before a line of code was written.

What we deliver — six practices, one team 03

Full-stack, end to end

The engineers who design the model also wire the devices, build the pipeline, and run it in production. No handoff between the team that sells and the team that ships.

01

Generative AI

Agents and copilots, retrieval-grounded systems, fine-tuning and evaluation, multimodal and voice interfaces.

Bedrock · Vertex · Azure OpenAI
02

Machine learning

Computer vision, forecasting and optimization, anomaly detection, speech and language at production scale.

SageMaker · Vertex AI
03

IoT & edge

Device fleets at scale, edge inference that works offline-first, digital twins, SCADA and OT integration.

IoT Core · Greengrass
04

Data & MLOps

Lakes, pipelines and governance, model CI/CD and registries, monitoring and drift detection, cost tuning.

Glue · Kinesis · Databricks
05

Conversational

Natural-language IVR, chat and voice assistants, and analytics over everything customers actually say.

Lex · Connect · Transcribe
06

Platform engineering

Multi-tenant SaaS, load testing, migration off proprietary stacks, DevOps automation and 24/7 support.

CDK · ECS · Serverless
Full capability sheet →
How we deliver — agentic lifecycle 04

Speed from the agents.
Accountability from the gates.

AI agents accelerate every phase of delivery. A named human closes each one. Agents never deploy themselves.

01

Discover

Use-case triage against value and feasibility; data audit; agents sweep your systems to map what exists.

Business case signed
02

Design

Architecture, evaluation design, and guardrail specification — written so agents and auditors can both execute it.

Architecture approved
03

Build

Agent teams implement, test, and document in parallel; engineers direct and review every change.

Eval suite passing
04

Evaluate

Offline evals, red-teaming, and user acceptance with your subject-matter experts on real data.

Quality bar met
05

Deploy

Staged rollout with human-in-the-loop first; integration hardening; operations training.

Readiness review
06

Operate

Monitoring, drift detection, retraining, and cost management as a running service.

Monthly value review
The engagement model →
Customer work — selected 05

Running systems, not pilots

Three of more than a hundred delivered engagements. Each replaced a manual process that had been treated as unavoidable. Customers are described, not named.

Food processingComputer visionEdge

Spoilage caught before it ships

A large-scale food processor running high-volume production lines.

Opportunity

Quality control was manual. Identifying spoilage, tracking SKUs, and assuring shipment quality across high-volume lines was slow and error-prone, with millions of dollars of product loss at risk annually.

Solution

ML models deployed at the edge via Greengrass and SageMaker, tracking produce across production lines in real time. Vision models trained to detect spoilage indicators and flag non-conforming product before shipment, with SKU-level analytics surfaced to operations.

Outcome

Approximately $50M in savings — product loss reduced, SKU tracking accuracy improved, and spoilage caught before distribution. Quality control moved from manual bottleneck to automated, scalable process.

TelecommunicationsComputer visionMEC

Driver safety on the edge

A global telecommunications operator running large commercial fleets.

Opportunity

Large commercial fleets needed real-time detection of unsafe driving. Traditional telematics lacked the visual intelligence to catch nuanced violations, and cloud-only processing added latency that made real-time alerting impossible.

Solution

In-vehicle cameras stream to a local cellular MEC node over AWS IoT, keeping data close to the source. Inference runs on the edge via Greengrass and SageMaker-trained models, with alerts routed straight back to driver and fleet manager.

Outcome

Near real-time visibility into driver safety across the fleet. The edge-first architecture holds up in low-connectivity areas — enabling proactive enforcement rather than post-incident review.

Financial servicesAgentic AIERP

Exceptions that resolve themselves

A major financial services firm.

Opportunity

ERP exceptions escalated to senior finance staff by default, pulling the most experienced people in the department into low-value matching work every close cycle.

Solution

An intelligent agent that detects exceptions across systems, categorises them by root cause, and resolves the routine ones autonomously — routing only genuinely ambiguous cases to a human, with the evidence attached.

Outcome

85% of exceptions resolved automatically, and month-end close accelerated by five days.

All customer work →
What happens next 06

Name the number.
Then we build.

Two weeks to a ranked portfolio and a pilot plan. Four to eight weeks to a working system on your data with a measured result attached. If we can’t name the number, we haven’t found the use case yet.

Book sprint zero → How the engagement runs →