Turn data into decisions and products
AI and Data Solutions
From LLM-powered assistants to predictive models and analytics pipelines, we help you put AI to work on real business problems.
Who this is for
AI and data engagements fit teams that have a specific workflow to automate or a product feature to ship, not a mandate to do something with AI. Buyers are product leaders adding an assistant to their software, operations leads automating support or back-office work, and companies that need reliable data pipelines before any model will work.
- SaaS products adding an AI assistant with retrieval over their own documentation and data
- Support and operations teams automating classification, routing and drafting with agents
- Businesses that need a data warehouse and pipelines before analytics or ML is possible
- Companies with a notebook model that has to become a monitored production service
How we approach AI and data solutions
Every AI feature starts with an evaluation set: 50 to 200 real examples with expected outputs, agreed with you before we pick a model. We build retrieval-augmented generation over your data with vector search, add guardrails for prompt injection and unsafe output, and put per-request token budgets and caching in place so cost per user is predictable. Models run in your cloud account or through your API keys, and prompts, outputs and costs are logged under your control.
For classical machine learning and analytics we start with data engineering: pipelines in Python and Airflow or dbt, a warehouse such as Snowflake or BigQuery, and data quality checks that fail loudly. Models are versioned, deployed behind an API with monitoring for drift and latency, and retrained on a schedule you set. We report accuracy, cost and latency on a dashboard from the first release so improvements are measured, not assumed.
What a typical engagement looks like
| Phase | Duration | What you get | Typical cost |
|---|---|---|---|
| Data and use-case assessment | 1 to 2 weeks | Data inventory, evaluation set, feasibility, fixed estimate | $3k to $8k |
| AI feature or assistant | 4 to 12 weeks | RAG or agent workflow, guardrails, cost controls, monitoring | $8k to $60k |
| Data platform | 6 to 12 weeks | Pipelines, warehouse, quality checks, BI dashboards | $20k to $60k |
| ML model to production | 6 to 10 weeks | Versioned model, API, drift monitoring, retraining schedule | $15k to $45k |
| Optimisation retainer | Monthly | Eval reruns, prompt and cost tuning, model upgrades | $2k to $6k per month |
Everything you need for data & ai, done properly.
LLM and AI agent development
Assistants, copilots and agents built on OpenAI, Anthropic and open models with retrieval over your data.
Machine learning solutions
Custom models that predict outcomes, detect patterns and automate decisions.
Data analytics and BI
Dashboards and reporting that turn raw data into action.
Natural language processing
Chatbots, sentiment analysis, classification and document understanding.
Computer vision
Image and video analysis for quality control, security and automation.
Data engineering
Pipelines and warehouses that collect, process and store data reliably.
How a typical engagement runs.
- 1
Data assessment
Evaluate your data assets, quality and infrastructure.
- 2
Strategy
Clear objectives and a roadmap for the AI implementation.
- 3
Data preparation
Clean, transform and structure data for analysis and training.
- 4
Model development
Build, train and validate models, or integrate foundation models.
- 5
Integration and deployment
Ship models into production inside your existing workflows.
- 6
Monitoring and optimization
Track performance and retrain to keep accuracy high.
Outcomes
What you get out of it
- Decisions backed by data instead of guesswork
- Repetitive work automated, operating cost down
- Customer behavior and demand predicted ahead of time
- Personalized experiences at scale
- Risks and opportunities spotted before competitors
- Value unlocked from data you already own
Technologies
Tools we reach for on data & ai work
- Python
- LangChain
- OpenAI
- Anthropic
- TensorFlow
- PyTorch
- Scikit-learn
- Apache Spark
- Databricks
- AWS SageMaker
- Snowflake
- Power BI
Products we've shipped, and what happened next.
Case studies written from the technical documentation of each project: the stack, the scale and the outcome.
Data & AI questions, answered.
Can you integrate ChatGPT or Claude into our product?
Yes. We build production-grade LLM features with retrieval over your data, guardrails, evaluation and cost controls, on OpenAI, Anthropic, Google or open-source models.
We do not have clean data. Can you still help?
Most clients start there. Data assessment and engineering is the first phase of every AI engagement.
How do you keep our data private?
Data stays in your cloud account, models can run in your VPC, and we sign a DPA and NDA before any access.
Specialised data & ai services
Related services