Agentic AI
Agents that read your systems, plan a next step, act, and check their own work — inside a policy you set and with a human on the approvals that carry risk.
Talk this throughArtificial intelligence
AI that changes a decision, not a slide
We start from a decision with a cost attached, build the smallest system that changes it, and put evaluation and guardrails around it before it touches a customer.
MWRKJT
Delivery leads online — talk to a human, not a botProdNot another pilot
100%Answers cited to source
0Training on your data
Agent operating loopHuman approval gate
100% delivery success — delivered worldwide across seven regions
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
What you get
Pilots are cheap. Production is the hard part.
Most AI programmes stall between the demo and the day the business depends on it. The gap is not the model — it is retrieval quality, evaluation, access control, cost per call and someone accountable at 2am.
- A named decision, a baseline and a target before any build
- Retrieval grounded in your documents, with citations on every answer
- Evaluation sets and regression tests, run on every change
- Guardrails, audit logs and a human approval gate where it matters
- Cost per interaction tracked from the first sprint
Evaluation dashboardAccuracy · cost · latency
Capabilities
Where AI earns its place
Five patterns cover most of what mid-market teams actually need.
AI Copilots
Assistants embedded where the work happens: the CRM, the ticket queue, the underwriting screen. Narrow scope, measurable handling time.
Talk this throughAI Strategy & Roadmap
A sequenced portfolio of use cases with value, feasibility and data-readiness scored, so you can defend the order to a board.
Talk this throughGenerative AI
Drafting, summarisation and synthesis wired into real workflows, with review steps and templates that keep output on-brand and on-policy.
Talk this throughRAG Systems
Retrieval over your own knowledge with citations, permissions inherited from source, and evaluation on answer quality — not vibes.
Talk this throughBy the numbers
MOD's AI work in numbers
Grounded in real AI products we have shipped, each with evaluation you can inspect.
0AI products shipped, from text-to-SQL to an AR + ML SDK
0Databases queried in plain English: Postgres, BigQuery, MySQL
0%Answers grounded and cited to a source document
0Client data used to train third-party models
Delivery model
How the work actually runs
One delivery model, whatever the engagement. You always know what is shipping this week and what it is worth.
01
Discovery
We map your sources, decisions and blockers, understand the business goal, and agree what success looks like before anything is built.
02
Solution design
We design the architecture, data models and use cases — right-sized to your team and your cloud — and cost the work before you commit.
03
Development
Weekly sprints against a visible backlog: pipelines, models, dashboards and applications, all in version control and yours from day one.
04
Testing & validation
Every pipeline and model is tested — with frameworks like PyTest and Great Expectations — so the numbers are trustworthy before they ship.
05
Deployment & training
We deploy into your cloud and train the people who will run it, so the capability lands with your team, not just our code.
06
Support & optimisation
Observability, alerting and continuous improvement keep the system fast, accurate and cost-efficient long after go-live.
Proof
Recent work
Marketing · EMEA
Marketing and CRM data unified across 20+ EMEA countries
A digital-transformation programme that consolidated marketing and CRM data across the region on Azure and Power BI, with a custom lead-management automation built on Power & Logic Apps.
80%Manual workload cut
20+EMEA countries
FMCG · Nestlé
Workforce analytics for Nestlé’s supply & demand planning
HR data marts on SAP feeding automated daily ingestions through AWS Glue and Databricks, on a medallion architecture, so planners could see available resource against production targets.
DailyAutomated ingestions
MedallionData architecture
Data strategy · Germany
One source of truth from 50+ scattered sources
Data strategy and cloud architecture on Google Cloud, ingesting 50+ sources through Stitch, modelled in dbt and surfaced to the CEO and Head of Product in Looker Studio.
50+Sources unified
ExecLive dashboards
FAQ
Questions buyers actually ask
Straight answers. If yours is not here, ask us on a call — we will tell you if we are the wrong fit.
Ask a questionNo. We deploy against enterprise endpoints with training disabled, or in your own cloud tenancy. Data residency and retention terms are agreed in writing before the first sprint.
Retrieval grounds every answer in your documents and the system cites what it used. Anything it cannot ground, it declines. We run an evaluation set on every release and publish the pass rate.
Because the first question we ask is which decision changes and what that is worth. If we cannot answer it with you, we tell you not to build — straight advice is the advice you are paying for.
You do — code, prompts, evaluation sets and infrastructure, in your repositories from day one. Your engineers pair with ours through the build so the handover is a formality.
Next step
Ready to find out what your data is worth?
Thirty minutes with a delivery lead, not a salesperson. You leave with a point of view on your highest-value use case and what it takes to ship it.