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Service · AI

AI features that survive contact with real users.

We build practical AI into products people already rely on — on-device models, assistants and automated workflows. Scoped honestly, measured against real data, and shipped only when it actually works.

Technologies

We use the tools your project actually needs.

Every stack pick is a judgement call — chosen to make your software fast to ship, fast to load, and easy to evolve.

Python
PyTorch
TensorFlow
Hugging Face
ONNX
LangChain
What we offer

Fine-tuning, embedded AI and agents that do real work.

Fine-tuning

We adapt open models to your domain, your tone and your data — so you get answers shaped by how your business actually works instead of generic output from a general-purpose model.

Embedded AI

Models quantised and packaged to run inside your app, on the device itself. No round trip to a server, no per-request bill, and sensitive data never has to leave the phone.

AI agents

Agents that carry out real multi-step work against your systems — with the tool access, guardrails and audit trail needed to let them run on live operations.

Our process

A predictable path from idea to shipped product.

Each engagement follows the same steps, so you always know where we are and what comes next.

01

Detailed consultation

  • We start by separating the problems AI actually solves from the ones it does not.
  • Data sources, privacy constraints and success criteria are documented up front.
  • We sign an NDA before any sensitive material is shared.
02

Feasibility spike

  • A short, paid spike to prove the approach works on your real data.
  • Model options compared on accuracy, latency and running cost.
  • You get an honest answer — including when the answer is not to build it.
03

Data and evaluation

  • Cleaning, labelling and structuring the data the feature depends on.
  • An evaluation set built before the model, so quality is measurable.
  • Baselines recorded so every later change can be judged against them.
04

Development

  • Active development on a scoped backlog in two-week iterations.
  • On-device or server inference chosen to match your privacy and cost profile.
  • A working build shared with you every two weeks.
05

Optimisation

  • Quantisation and model compression to fit real devices.
  • Latency budgets on inference and cold start.
  • Token and compute costs tracked as a first-class requirement.
06

Testing

  • Automated evaluation runs on every change to catch quality regressions.
  • Adversarial and edge-case prompts tested before release.
  • Human review on the outputs that carry the most risk.
07

Deployment and monitoring

  • Rollout behind feature flags with a clear fallback path.
  • Output quality, latency and cost monitored after launch.
  • Routine maintenance as models and dependencies move.
Why pick Owjar

What you actually get for working with us.

On-device where it counts

We ship quantised models that run on the device — lower latency, lower running cost, and data that never has to leave the phone.

Measured, not guessed

Every feature gets an evaluation set before it gets a model. You see quality as a number, not as a demo that happened to go well.

Your data stays yours

We design around your privacy constraints from the first call, and we never train on your data without explicit written agreement.

Built into the product

AI that lives inside the flows people already use — not a chatbot bolted onto the corner of an app to tick a box.

Get in touch

Let's talk about your project.

Tell us where you are stuck and we will tell you exactly how Owjar can help.

Let’s build something worth shipping.

Tell us what you’re building. We’ll tell you honestly whether we’re the right team for it.

Book a consultation