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AI Engineering

AI Engineering, from Data to Production Systems.

Generative AI, retrieval and models, taken from prototype to production.

AI engineering services take generative AI, retrieval and models from a working prototype to a dependable system in production, connected to the data and software an organisation already runs. Solonomous provides them as one discipline: data and knowledge, model selection and adaptation, multimodal systems, inference, evaluation and controls.

It is the engineering foundation under both of our businesses: our own AI-native products, and the forward-deployed engineering we do inside organisations.

Everything around the model, engineered

The engineering sits around the model: retrieval that respects permissions, models chosen on your own tests, serving sized to the budget, and tests that run before every release.

Answers from our manuals, scans and call recordings

  • Hands on a tablet and laptop, with floating panels of yellow file folders

    RAG Pipelines

    Answers from your manuals

  • Hands with a stylus beside floating profile documents and a checklist.

    Document Intelligence

    Scans read into fields

  • Man holding a smartphone beside floating chat bubbles and call icons.

    Speech & Voice AI

    Calls transcribed and searched

  • Hand reviewing floating profile cards with star ratings above a tablet.

    AI Evaluation, Safety & Security

    Every answer tested first

Where a problem lands

Most problems draw on more than one discipline. A plain description of the work points to the service pages that cover it.

  • AssistantThe policy does not say. I have flagged the question for the warranty team.
  • OperatorDoes that still apply if the unit was serviced elsewhere?
  • AssistantThe warranty policy covers this part for parts and labour, as its repairs section says.

Grounded, or it says so

The model answers from retrieved sources, the sources travel with the answer, and a gap in your records is named, not filled.

Customer reply, refund wording

We are sorry for the delay. Your refund for the damaged unit is approved and will reach your account.

Checked before it is used

Output with legal, financial or customer-facing effect waits for the person accountable for it, with its sources beside it.

  1. Release 12Live
  2. Release 13Passed
  3. Release 14Held back
  4. Release 15Testing

Every change versioned

Prompts, models, retrieval settings and datasets are versioned, scored against the suite before they ship, and rolled back when a release degrades.

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Services

Services in AI Engineering

Each discipline has its own service page, with the detail of how it is built and delivered.

Model Engineering

Enterprise Knowledge

Multimodal AI

AI Infrastructure

The work between prototype and production

A prototype shows a model can do the task. Production needs everything else: the data it reads, the model it runs, the way it is served and the tests it must pass.

  • Generative AI, engineered into systems that are checked

    We engineer generation in four places. Grounding: the model answers from retrieved sources, and the sources travel with the answer. Structure: output is constrained to schemas a downstream system can validate. Checks: rules, second models and evaluation sets catch what should not pass. Approval: anything with legal, financial or customer-facing effect waits for the person accountable for it.

  • Data and enterprise knowledge

    Connectors, pipelines and retrieval that make an organisation's records usable by a model, with permissions intact. Permissions travel with the data. A user who cannot open a file in the source system does not receive it through an answer. Lineage records where every retrieved passage came from, so an answer can be traced to its source and corrected there.

  • Model selection, adaptation and evaluation inside a solution

    Selecting, integrating, evaluating and, where a solution needs it, adapting existing models. Model work is a capability inside the solutions we engineer and the products we build, not a separate business. Every choice is measured on the task's own evaluation set, so a model is chosen on evidence rather than reputation.

  • Vision, speech and documents in the same pipeline

    Vision, speech and documents engineered into the same pipelines as text. Each modality brings its own constraints. Vision depends on lighting, camera position and the cost of a missed defect. Voice depends on latency, interruptions and accents. Documents depend on layout, handwriting and the fields a downstream system will accept. We design for those constraints before choosing a model.

  • Inference, serving and release control

    A model in production is a service with a budget. We engineer the serving layer to match the workload: routing each request to the model that fits its latency, accuracy and cost, caching and batching where they pay off, and choosing hosted, open-weight or private deployment by privacy, quality and economics.

  • Evaluation first, and the controls around it

    The harness a system is judged by, and the safeguards around it. Controls are designed in from the start: least-privilege access to data and tools, secrets kept out of prompts, defences against prompt injection, red-team exercises before launch and a plan for incidents. We describe the engineering principles and tested implementations we apply; we do not claim certifications we do not hold.

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The foundation

One engineering foundation under two businesses

Solonomous runs two core businesses. The first builds our own AI-native products and platforms. The second is forward-deployed engineering: our engineers working with an organisation's teams, systems and data until a solution is in production. Both depend on the same engineering, and this page maps the part of it that turns data and models into working systems.

The shared foundation is named in one line: Generative AI, Agentic AI, Autonomous Systems, AI Engineering, Infrastructure, Integrations and Platform Engineering. Agents and their operating boundaries have their own page, Autonomous Systems; multi-tenant platforms have theirs, AI SaaS & Platforms. This page covers the rest: the data, retrieval, model work, multimodal systems, serving and evaluation that all of them depend on.

All services

Proved on your work, then shipped

Most engagements begin with one system and the data it needs. We build it in your environment, judge it on your own examples and hand it to the people who will run it.

  1. 01

    Scope

    We read the data, the systems and a sample of the task, then agree the evaluation set and the thresholds a release must pass.

  2. 02

    Build

    Retrieval, model choice, structured output and serving, built in your cloud or on your premises and connected to the systems of record.

  3. 03

    Prove

    The system runs against the evaluation set and adversarial cases before release, then with a small group of users and a rollback ready.

  4. 04

    Hand over

    Runbooks, the evaluation harness, versioned prompts and pipelines, and a trained team. Ownership is set out in the contract before work starts.

The same engineering, in both businesses

AI engineering: common questions

What does an AI engineering company build for an enterprise?

Working systems rather than demonstrations: a retrieval service that answers from the organisation's own records, a document pipeline that posts checked data into a system of record, a voice or chat interface connected to the tools behind it, and the serving, evaluation and controls those need in production. Solonomous builds these inside its own products and, through forward-deployed engineering, inside customer organisations.

What is the difference between RAG and fine-tuning, and when is each the right choice?

Retrieval-augmented generation (RAG) gives a model the right information at the moment it answers, drawn from your documents and systems, with the source attached; it suits knowledge that changes and answers that must be traceable. Fine-tuning adjusts an existing model's behaviour, such as its format, tone or handling of a narrow task, by training it further on examples. Most enterprise systems start with retrieval and good instructions; fine-tuning is added when evaluation shows they are not enough and there is data to train on. The two are often combined.

When does a solution need an adapted or specialised model rather than an existing one?

When the task's evaluation set shows that an existing model, properly grounded and instructed, still misses the behaviour required; when a narrow job runs at a volume where a smaller model is meaningfully cheaper or faster; or when data must stay inside an environment where only an open-weight model can run. Otherwise an existing model is usually the simpler and safer choice.

How is an AI system evaluated before it reaches production?

Against a test set built from real examples of the task and agreed with the people who own the work, scored on accuracy, grounding, format, latency and cost, and exercised with adversarial cases. Thresholds are set before the first release, the suite runs on every change, and the same measures keep running in production.

Where does generative AI fit inside an enterprise system?

As one component among several. A generative model drafts, summarises, classifies, extracts or answers; retrieval supplies what it should know; schemas and checks constrain what it may produce; integrations carry the result into the systems people use; and approvals keep consequential output with the person accountable for it. The value comes from the whole system, not from the model alone.

Who owns the code, the data pipelines and any adapted model when the work ends?

Ownership is set out in each engagement's contract before work starts, including anything that remains under a third-party model's licence. We do not publish a blanket promise here, because the answer depends on the work, the models used and your requirements.
Laptop with a floating code window, gear and chart tiles.

Bring the problem. We engineer the system.

Tell us what the system must do, what it must connect to and who decides. We will come back with an engineering view of how to build it.

  1. 01What the system must do, with a few real examples
  2. 02Where the data lives, and who may see it
  3. 03How you would judge a good answer
Talk to Our Engineering TeamForward-Deployed Engineering