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Invictt AI

How we work

Business problem to production system, in seven stages.

The same shape applies whether the system is an AI pipeline, a piece of custom software, or the integration layer between them.

A man holding his head at his desk, looking overwhelmed in front of his laptopWhere most engagements start

01/The process

Seven stages, and a real chance of being told no in week one.

Some engagements stop at stage one — that's the point of it.

01

Understand

Week 1

We sit with the people doing the work and measure what it actually costs — before anyone mentions a solution.

We map the real process, exceptions included, and measure volume and time-per-step. If it's not worth solving with engineering, we say so here.

You get

A measured baseline and a go / no-go recommendation in writing.

02/Where it runs

On your hardware, in a cloud, or a deliberate mix.

Decided per component, not as a blanket policy.
YOUR INFRASTRUCTUREUserApplicationAI ModelDatabase

Nothing leaves the building

Inference, embeddings and storage run on hardware you own — no vendor egress, no data-residency question to answer.

Best for

  • Regulated sectors and confidential documents
  • High, steady volume where per-token pricing stops making sense
  • Sites with poor connectivity or hard latency budgets
How this works

03/Engineering & principles

Built by the people who scope it, on a real stack.

Chosen per problem, not sold as a package.

Software & platforms

The application layer everything else is built on.

  • TypeScript
  • Python
  • React
  • Next.js
  • FastAPI
  • Postgres

Models & training

What we train, fine-tune and serve.

  • PyTorch
  • Hugging Face
  • LoRA / QLoRA
  • YOLO
  • Whisper
  • ONNX
  • TensorRT
  • vLLM

On-Prem & private inference

For when data cannot leave the building.

  • Ollama
  • llama.cpp
  • vLLM
  • Qdrant
  • pgvector
  • NVIDIA Jetson
  • On-prem GPU

Agents & orchestration

How models get access to real systems, safely.

  • MCP
  • LangGraph
  • Structured outputs
  • Eval harnesses
  • OpenTelemetry

Automation & integration

The pipelines that carry the work.

  • n8n
  • Redis
  • Celery
  • OAuth / OIDC
  • Webhooks

Delivery & operations

How it stays up after we leave.

  • Docker
  • Kubernetes
  • GitHub Actions
  • MLflow
  • Prometheus
  • Grafana

How we work

You choose where it runs
On-prem, cloud, or a deliberate mix, decided per component. We build all three, so the recommendation isn't just whatever matches our own stack.
Boring problems pay better
Invoice entry, inbox triage, internal tooling — high volume, low glamour, measurable within a quarter. We'd rather build that than a demo.
Measured, not asserted
A result is a number on your data, or it's marketing. Every engagement ends with a measurement against the baseline — even when the honest answer is that we didn't beat it.
Humans on the exceptions
Full automation of an ambiguous task just automates the mistakes. We route genuine ambiguity to a person, with an interface built to make reviewing fast.
No lock-in
You own the code, the weights, the datasets and the documentation — built so your team can change a rule without calling us.
We will talk you out of things
Some of what gets asked for isn't worth building. Saying so in week one is cheaper for everyone than discovering it in month four.
A professional reviewing work at her desk, thinking through a decisionWhat it looks like once it's running

Ready to start with stage one?

The first conversation is scoping, not a pitch — replies within one business day.

Start a conversation