AI & Machine Learning Development
AI that survives contact with production. We start from the task you want automated and the data you actually have, then build the smallest system that does the job reliably.
That often means retrieval over a well-organised knowledge base rather than a fine-tune, and a clear evaluation harness rather than a demo that impressed everyone once.
What We Deliver
The work this engagement actually covers — and what you own at the end of it.
LLM & RAG Integration
Retrieval-augmented systems grounded in your own documents and data, with citations, guardrails and a measured answer-quality baseline.
Predictive Modelling
Forecasting, churn prediction, demand planning and scoring models trained on your historical data and monitored for drift after deployment.
Computer Vision
Image classification, object detection, OCR and document extraction — on-device or in the cloud depending on latency and privacy needs.
Intelligent Automation
Document processing, classification, routing and back-office workflows where the manual version is slow, expensive and error-prone.
Data & Feature Pipelines
The unglamorous foundation: ingestion, cleaning, labelling and feature storage. Most failed AI projects fail here, not in the modelling.
Evaluation & Monitoring
Test sets, human review loops, cost and latency tracking, and alerting when quality drifts — so you know the system still works next quarter.
What you receive
- Deployed model or AI service running in your infrastructure
- Data and feature pipelines with documented lineage
- Evaluation harness with a measured quality baseline
- Prompt, retrieval and model configuration under version control
- Cost, latency and quality monitoring dashboards
- Written limitations and failure-mode documentation
How the Work Runs
No stage is a surprise, and every one of them ends in something you can look at.
Use-Case Assessment
We pick the task by value and feasibility, and we will say plainly when conventional software would solve it better and cheaper.
Data Readiness
An honest audit of the data you have — volume, quality, labelling, licensing and privacy — because that decides what is achievable.
Prototype & Evaluate
A working prototype measured against a test set and a human baseline, with the accuracy, cost and latency numbers on the table.
Productionise
Deployment behind proper APIs with rate limits, fallbacks, caching, audit logging and human-in-the-loop review where the stakes require it.
Monitor & Improve
Drift detection, quality sampling and cost tracking after launch, with retraining or retrieval tuning on a defined cadence.
What Good Looks Like
Every engagement includes an evaluation harness, because "it seemed to work when we tried it" is not a quality bar you can run a business on — and because it is the only way to tell whether a change improved anything.
Technology We Use
Chosen for what your team can hire for and maintain, not for what is new this quarter.
- OpenAI
- LangChain
- TensorFlow
- PyTorch
- Vertex AI
- Hugging Face
- Vector Databases
- Python
- Pandas
- scikit-learn
- MLflow
- Computer Vision
Engagement Models
Three ways to work with us. Which one fits depends on how settled your scope is and how long you need the team.
Fixed-Scope Project
A defined outcome, an agreed timeline
We scope the work up front, agree the deliverables and the date, and carry the delivery risk. Best when you know what you need built and want a predictable commitment.
Best for
- A launch with a hard deadline
- A well-understood rebuild or migration
- First projects with a new partner
What's included
- Written scope, milestones and acceptance criteria
- A named project lead and weekly demos
- Fixed delivery date with change control
- 30 days of complimentary post-launch support
Dedicated Team
Our engineers, your roadmap
A ring-fenced team — engineers, designers and QA — working only on your product, in your rituals and your tools. You set the priorities sprint by sprint.
Best for
- Evolving product roadmaps
- Scaling an in-house team quickly
- Long-running platform work
What's included
- Named team members, not a rotating pool
- Your sprint cadence, standups and board
- Direct access to every engineer on the team
- Scale the team up or down at each sprint boundary
Ongoing Retainer
Someone who already knows your system
A monthly block of engineering time for maintenance, security patching, performance work and steady improvement — from the people who built it.
Best for
- Live products that need care, not a rebuild
- Security and dependency upkeep
- Incremental features after launch
What's included
- Essential, Growth and Enterprise tiers
- Monitoring, security patches and dependency updates
- Agreed response times, up to a 24/7 SLA
- Monthly report on what changed and what it cost
AI work usually begins with a fixed-scope prototype and evaluation. Systems that go to production need ongoing tuning, so a retainer typically follows.
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AI & Machine Learning Questions
The things prospective clients ask us before committing to this work.
Can you integrate AI and machine learning into our existing application?
Absolutely. Aquison Technologies integrates AI capabilities including natural language processing, recommendation engines, predictive analytics, computer vision and generative AI features. We work with TensorFlow, PyTorch, OpenAI APIs and Google Vertex AI. Integration into existing systems typically takes 4–10 weeks depending on data readiness.
How much data do we need before AI is worth doing?
It depends on the approach. Retrieval-augmented generation over your documents can work from day one because it uses a general model plus your content. Custom predictive models generally need thousands of labelled examples with real signal in them. The data readiness audit gives you a straight answer before you commit to a build.
How do you stop an AI feature from producing confident nonsense?
Ground responses in retrieved source material and show the citations; constrain the output format and validate it; add human review where a wrong answer is expensive; and measure quality continuously against a fixed test set. We also document what the system cannot do, which matters as much as what it can.
Will our data be used to train someone else’s model?
Not without your explicit decision. We use enterprise API tiers with training disabled by default, keep data in your chosen region, and can run open-weight models entirely inside your own infrastructure where regulation or policy requires it. Data handling is agreed in writing before any integration work starts.
What does it cost to run once it is live?
Inference cost is a real operating line, so we model it during the prototype: cost per request, expected volume, and the effect of caching and prompt design. You get projected monthly running cost before production, and dashboards tracking it afterwards.