AI & Machine Learning Consulting
We build production-grade AI and ML systems that go beyond prototypes — deployed, monitored, and delivering measurable ROI inside your business.
40+ enterprise clients · 150+ AI & data projects shipped
From AI experiment to production system
Most AI initiatives die in PoC purgatory — impressive demos that never make it past the pilot phase because the underlying data, infrastructure and governance aren't ready for production. Digitec is different. We've been building intelligent systems since before "GenAI" was a marketing category, and our engagements are designed to ship — not to dazzle.
We architect AI products end-to-end: from data readiness assessments and feature engineering through model selection, fine-tuning, retrieval-augmented generation, evaluation harnesses, deployment and continuous monitoring. Whether you need a custom predictive model trained on your proprietary data, an LLM-powered copilot wired into your CRM, or an agentic workflow that automates a complex back-office process, we bring the engineering rigour to make it real.
Our practice spans the full modern AI stack — Azure OpenAI for enterprise-grade privacy and compliance, Anthropic and OpenAI for frontier reasoning, open-source models for cost-sensitive workloads, vector databases like Pinecone and pgvector for semantic retrieval, and MLOps platforms for the discipline that keeps models accurate over time. Every solution is built with safety, observability and governance baked in from day one.
What we do
A full-stack practice built to solve the hardest digital problems with craft and precision.
Predictive Models
Forecasting, classification and anomaly detection trained on your own data. Built with proper evaluation, drift monitoring and retraining loops baked in.
Generative AI & RAG
LLM-powered assistants, copilots and content engines grounded in your knowledge base. Retrieval, evaluation harnesses and safety controls included by default.
Intelligent Automation
Agents and pipelines that automate complex back-office workflows end-to-end. We replace brittle RPA with reasoning agents wired into your existing systems.
Computer Vision
Image and video understanding for defect detection, surveillance, OCR and visual search. Production-ready models with edge or cloud deployment.
MLOps & Platform
Training, deployment, monitoring and retraining as a continuous engineering practice. We set up the platform so every future model ships safely and quickly.
Data Engineering for AI
Feature stores, vector databases and serving layers tuned for ML and GenAI. We make sure the data foundation is ready before a single model is trained.
How we work
Every engagement follows a battle-tested rhythm — clear, collaborative, and outcome-driven.
Discovery
We audit your data readiness, surface the highest-ROI use cases and pressure-test feasibility. You leave week one with a prioritised AI opportunity map.
Strategy
We design the target architecture, model choice, evaluation criteria and rollout plan. Budget, risk and adoption are scoped before a single line of code is written.
Build
Senior engineers ship a working prototype in 4–6 weeks, then harden it for production with evals, guardrails and observability. You see real outcomes, not slideware.
Launch & Optimise
We deploy, train your team, and stay on to monitor drift, retrain models and expand the use cases that are working. Performance compounds, it doesn't decay.
Why teams choose Digitec
Three reasons clients pick us over consultancies, freelancers and in-house builds.
We ship to production — not to demos
Most AI consultancies stop at the PoC. Our engagements are structured around a working production deployment with monitoring, evals and a clear ROI metric — typically inside 12 weeks.
Responsible AI is the default, not the upsell
Every system we ship includes evaluation harnesses, hallucination guardrails, audit logging and human-in-the-loop controls where it matters. Compliance reviews don't surprise us.
Model-agnostic, outcome-anchored
We work fluently across Azure OpenAI, Anthropic, OpenAI and open-source models. The model is picked on cost, latency and privacy fit for your use case — not on what we sell.
Technologies we master
"Digitec turned a 15-year-old data stack into a real-time AI engine — on time, on budget, with zero downtime."
Connected to every part of your stack.
Every Digitec engagement spans services, technologies, industries and measurable outcomes — explore how the pieces connect.
Common questions
How quickly can we get from idea to a working AI system?+
Most engagements ship a working production prototype in 6–12 weeks. We compress the loop by starting with use cases where your data is already AI-ready and scaling out from there.
Do you build with open-source or proprietary models?+
Both. The choice is driven by cost, latency, privacy and reasoning requirements — not by what we'd prefer to sell. We're equally comfortable with Azure OpenAI, Anthropic, OpenAI and open-source models like Llama and Mistral.
How do you handle data privacy and compliance?+
We default to private deployments on Azure OpenAI with dedicated tenancy, zero data retention and SOC 2 / ISO 27001 alignment. For regulated industries we add audit logging, PII redaction and human review gates.
How do you prevent hallucinations and quality drift?+
Every system we ship includes an evaluation harness with golden datasets, retrieval guardrails, content filters and continuous monitoring. We measure accuracy weekly and retrain whenever quality regresses.
Can you integrate AI into our existing systems?+
Yes — most of our engagements integrate with CRMs, ERPs, data warehouses, helpdesks and internal tools. We meet your stack where it is rather than forcing a rebuild.
Related insights
Deep-dive articles from the Digitec team on ai & machine learning consulting — written by the practitioners who ship the work.
Ready to start?
Bring us your goal, your stack, or your roadblock. We'll spend 30 minutes mapping the fastest path to outcome — no slide deck, no pitch.
Or email us at info@digitecsolution.com