Suntra AI
Book a discovery callBook a call

AI · Machine learning · Computer vision · Software

Production AI, built by people who have shipped it.

Suntra AI is a consulting studio for teams that need machine learning and computer vision running in the real world: on cameras, on edge GPUs, inside the products your customers already use. We scope, build, deploy and hand over.

Book a discovery call See what we build

Multi-object detection and tracking, the kind of pipeline we tune to run at 30+ streams per GPU.

Edge to cloudJetson, DeepStream, Triton, Kubernetes
Weeks, not quartersFirst working pipeline inside a 2-week sprint
MeasuredmAP, latency and throughput reported on your data
Yours to keepCode, models and docs handed over, no lock-in

Services

Four practices, one team

Most engagements touch more than one. A vision system needs a backend, a model needs an evaluation harness, and all of it needs to run somewhere on a budget.

01 / COMPUTER VISION

Computer vision systems

Detection, tracking, segmentation, OCR and video analytics that hold up on real cameras, in bad light, at scale.

  • Multi-camera detection and multi-object tracking
  • Real-time video pipelines on NVIDIA DeepStream and GStreamer
  • Camera calibration, 3D tracking and bird's-eye-view mapping
  • Vision-language models for search, alerts and summarization
02 / MACHINE LEARNING

Machine learning and applied AI

From a dataset and a question to a model that is evaluated, versioned and monitored.

  • Custom model training and fine-tuning
  • LLM and agent integrations with retrieval and tool use
  • Evaluation harnesses, labeling strategy and data pipelines
  • Model optimization: quantization, TensorRT, ONNX export
03 / SOFTWARE

Software engineering

The APIs, services and interfaces that make a model useful to someone who is not a data scientist.

  • Python and C++ backends, FastAPI and gRPC services
  • Microservices, Kafka event streams, Docker and Kubernetes
  • Dashboards and operator tools for inspecting results
  • Code review, architecture and performance work on existing systems
04 / STRATEGY

AI strategy and feasibility

Before you fund a build, find out whether it will work and what it will cost to run.

  • Feasibility studies with a working prototype, not a slide deck
  • Build, buy or fine-tune decisions with real benchmarks
  • Hardware sizing: cameras per GPU, latency budgets, cloud cost
  • Technical due diligence for investors and acquirers

How we work

Prototype early, measure everything, hand over cleanly

Week 1 · Discover

Scope against your data

We look at your footage, data and constraints and agree on a target metric and a hardware budget before writing code.

Week 2–3 · Prototype

A pipeline that runs end to end

A first version on your real inputs, with a baseline number. Rough, but honest about what is hard.

Week 4+ · Build

Iterate toward the target

Model, data and system work in short cycles. Every change is benchmarked on the same evaluation set.

Final · Deploy and hand over

Production, then your team

Containerized, monitored, documented. We train your engineers and stay available after launch.

Stack

Tools we reach for

We pick for your constraints, not our habits. This is what we know well enough to debug at 2 a.m.

Vision and video

DeepStreamGStreamerOpenCVYOLODETRSAMRTSP

Models and training

PyTorchHugging FaceTensorRTONNXTritonvLLM

LLMs and agents

Claudeopen-weight VLMsRAGMCPeval harnesses

Platform

PythonC++FastAPIKafkaDockerKubernetesJetsonAWS / GCP

Engagements

Three ways to work with us

2 weeks · fixed price

Discovery sprint

A feasibility answer with evidence: a prototype on your data, a baseline metric, and a costed plan.

  • Data and infrastructure review
  • Working prototype pipeline
  • Benchmark report and architecture proposal

Fits: you have an idea and footage, and need to know if it is worth funding.

6–16 weeks · milestone based

Build and deploy

We deliver the system end to end, from model to running service, against agreed metrics.

  • Model development and optimization
  • Production pipeline and APIs
  • Deployment, monitoring and handover training

Fits: a scoped product feature or internal system with a launch date.

Monthly · retained

Embedded engineering

A senior engineer inside your team, in your repos and standups, for as long as you need the capacity.

  • Part-time or full-time
  • Architecture and code review
  • Mentoring your ML and backend engineers

Fits: an existing team that needs depth in vision, ML or systems.

Principles

What you can expect

Consulting goes wrong when incentives drift. These are the rules we hold ourselves to on every engagement.

  1. Numbers on your data, not ours.Every claim about accuracy or speed comes with the evaluation set and the hardware it ran on.
  2. The simplest model that meets the target.A tuned detector often beats a bigger one. We earn complexity, we do not start with it.
  3. You own everything.Source, weights, training scripts, infrastructure as code and the docs to run it without us.
  4. Senior people do the work.The engineer on the call is the engineer in the repo.

Contact

Tell us what you are trying to build

Send a few lines about the problem, the data you have and the timeline. We reply within two business days with questions or a proposed first call.

hello@suntra.ai

Prefer email? Write to the address above. NDAs are fine before we look at anything sensitive.

Thanks. Your message is on its way. Expect a reply within two business days.

We only use your details to reply to this inquiry.