AI Engineering Services
Your data has the answers. Getting to them is the problem.
Bespoke data infrastructure, pipeline architecture, and AI integration — built around the systems you already run. No rip-and-replace. No vendor lock-in. First working system in 30 days.
Three Data Blockers We Remove
Five years of sales history locked in a legacy ERP — your forecasting model can't reach it.
Three teams running three versions of the same KPI dashboard. None agree. Nobody trusts any of them.
AI pilot worked in a sandbox. Production deployment hit integration issues IT estimated at 18 months to fix.
We fix all three — on your existing stack, in 30 days.
What is AI Engineering?
AI engineering is the discipline of building production-ready AI systems that work inside your actual business environment. It is not data science. It is not software development. It is the layer between a promising AI prototype and a system your operations team relies on every day — connecting data sources, building clean pipelines, embedding governance, and ensuring the AI your board approved actually reaches production.
Most AI projects skip this step. Models get built in isolation. Data stays in silos. The model performs in testing and breaks the moment it touches real operational data. AI engineering solves this by starting with your data reality — not the AI fantasy.
Why AI Projects Fail
Three patterns we see across £100M+ companies — before they call us.
The Pilot Trap
£150K pilot. 85% accuracy in testing. Six months later, still a pilot.
Production deployment exposed data quality issues nobody anticipated. The model worked in a controlled environment. It had no data foundation to stand on in the real one.
Cost: £150K + board credibility
The Integration Nightmare
Vendor delivers the model. IT discovers the data formats don't exist.
Integration estimate: £300K and 18 months. AI project dies in procurement. Technically elegant — and completely disconnected from the systems your business actually runs on.
Cost: £300K estimate + a year wasted
The Black Box Problem
AI works. First wrong decision — operations switches it off permanently.
Without data lineage and governance built in from day one, no operations team trusts an AI system long enough for it to prove its worth. £200K becomes a spreadsheet backup.
Cost: £200K + lost operational trust
The common thread: no AI engineering foundation. That is what we build.
What We Build
Five AI engineering capabilities, delivered as one integrated engagement.
Each capability below is available as part of a bespoke engagement — scoped to your infrastructure, your data complexity, and your AI goals. No generic packages.
Data Infrastructure & Pipeline Architecture
We build the connections your AI models need without forcing you to change the systems your operations depend on. Your ERP connects to your forecasting model. Your WMS feeds your demand planning AI. Your promotional data feeds your pricing engine. One team owns the full build — no handoffs between a data team, an AI team, and an IT team each running their own six-month timeline.
Unified Data Platform
Your data lives in at least six places. Sales in Salesforce. Inventory in SAP. Customer feedback across three systems. Marketing spend across six platforms. Operations in local spreadsheets. We build the connections so one query surfaces demand signals from all five sources, one dashboard shows margin impact across your entire product range, and one API feeds AI models from your complete data picture.
AI Navi delivery: Demand forecasting was running in Excel. After building a clean, centralised data pipeline, the first AI-driven forecast ran alongside the manual process in six weeks. In three months, the manual version was retired. The business case wrote itself — because we showed the ROI first, not promised it.
Data Governance, Quality & Lineage
AI systems fail when data quality fails. Governance built into the foundation is the difference between an operations team that trusts an AI system and one that quietly switches it off after the first error. We build it in — not bolt it on once problems surface.
Leadership-Ready Reporting & Dashboards
Your board doesn't want to hear about model accuracy. They want to understand business impact. We build dashboards that translate AI outputs into the language your leadership team uses — live, in minutes, not a reporting cycle later.
AI-Ready Data Foundation
Most companies approach AI in the wrong order: build the model, worry about data later. We build the data architecture first — so when your next AI initiative gets approved, the foundation is already there. Pilot to production in weeks, not months.
Your Stack. Our Expertise.
We work with the infrastructure you already run.
We don't sell technology. We integrate with yours. Cloud-agnostic, ERP-compatible, database-flexible — no vendor lock-in.
Cloud
AWS · Azure · Google Cloud · On-premises · Hybrid
ERP / CRM
SAP · Oracle · Microsoft Dynamics · NetSuite · Salesforce
Databases
SQL Server · Oracle DB · PostgreSQL · MongoDB · Snowflake · Redshift
Analytics
Tableau · Power BI · Qlik · Looker · Custom dashboards
AI Engineering in Practice
The same data engineering approach. Different industries.
Finance / Professional Services
AI-Powered Talent Matching — Working System in 5 Days
A firm spent days manually sourcing freelancers with frequent skill mismatches. The data existed across separate systems — it just wasn't connected. We built AI-powered matching that integrated skills, availability, rates, and behavioural vetting signals into a single pipeline.
→ Manual sourcing reduced from days to seconds
Digital Health
Patient Self-Service Platform — Prototype in Under Two Weeks
A diagnostics provider had booking, lab results, and patient data in entirely separate systems. Patients couldn't self-serve. We connected the data foundation and layered an AI assistant on top — patients can now browse, book, and track results through conversation.
→ Prototype live in under two weeks. Featured at major AI healthcare exhibition.
Full results library including SalesGenius.ai and ApplyGenius.ai → see all case studies on the homepage.
Custom Pricing
Priced to your infrastructure and scope. No generic packages.
Every AI engineering engagement is scoped to your specific data complexity, existing systems, and AI goals. You'll have a specific price within 48 hours of a 30-minute assessment call.
Typical Engagement Range
£25K – £150K
Complete AI engineering foundation
For comparison: an AI consulting firm engagement starts at £200K–£500K minimum with a 14-week mobilisation period. A full-time CAIO hire costs £250K–£400K+ per year and takes 4–6 months to appoint. We price significantly below both — with deeper sector and data engineering credentials than either.
Data Complexity
Number of source systems, data formats, volume, and current data quality.
Integration Requirements
Existing systems, security constraints, compliance needs — SOC 2, GDPR, food safety, audit trails.
AI Scope
Single use case (demand forecasting, route AI) or multiple applications across business units.
Timeline
Standard 30-day delivery or accelerated deployment for board deadlines and PE 100-day windows.
Support Level
Launch-only handover or ongoing optimisation as AI systems improve with live data.
Sector Depth
CPG / FMCG and Logistics engagements benefit from insider infrastructure knowledge. No learning curve on your dime.
Included in Every AI Engineering Engagement
AI Engineering can be delivered as a standalone engagement or as the Execute layer within the AI FlightPath™ Sprint (£15K–£25K fixed) — which adds AI strategy alignment and change management on top of the data engineering foundation. See all service tiers →
Common Questions
What CPG and Logistics leaders ask before we start.
Get Started
30 minutes.
Custom pricing within 48 hours.
We review your current data environment, understand your AI goals, and give you specific scoping and pricing — no pitch. You'll leave knowing exactly where to start and what it will cost.
No obligation beyond the call. Haja and Abhishek take every assessment personally.