Put AI where it moves business metrics — not where it makes a good demo. Practical, measured integration of LLMs and ML into real workflows.
AI Implementation is the practical work of putting machine learning, LLMs, and data-driven automation into your product and operations — without science-fair demos, shadow IT, or six-month research detours. We scope what actually moves a number, build it into production systems your team already uses, and stay accountable for whether it works.
We start where the value is — not where the model is. A short, honest scoping engagement identifies the two or three use cases with real ROI and kills the ones that are just ‘AI theatre’ before they waste quarters.
Production-ready features built on OpenAI, Anthropic, or open-source models: chat, copilots, content generation, summarisation, classification, retrieval-augmented search. With evaluations, guardrails, and cost control from day one.
Forecasting, recommendation, anomaly detection, and scoring models trained on your data — deployed with proper MLOps (feature store, monitoring, retraining) so the model doesn’t silently rot in production.
AI-assisted workflow automation that takes the repetitive knowledge work off your team — document processing, routing, triage, summarisation — wired into the tools they already use.
A ranked shortlist of use cases with expected impact, effort, data readiness, and risk — enough to have a real conversation with your CFO about where to spend the AI budget.
A working feature in production — inside your product or your internal tools — with evaluations, guardrails, and observability. Not a Jupyter notebook in someone’s Google Drive.
Offline eval suite, online metrics, hallucination and drift monitoring, cost dashboards — so ‘is it still working?’ has an answer other than ‘user complaints are down this week’.
Prompt library, data contracts, retraining playbook, and the briefing your team needs to own the feature after we hand over. No tribal knowledge, no ‘call us every time it drifts’.
Every engagement ends with something in production that moves a measurable number — conversion, response time, cost per ticket — not another PowerPoint with model accuracy plots.
Repetitive knowledge work — triage, classification, summarisation, document parsing — gets automated back to the system, freeing 40% or more of the workflow it used to consume.
AI features embedded in your product — search, copilots, recommendations — lift engagement and retention without requiring users to learn a new workflow. The AI serves the UX, not the other way around.
Token budgets, caching strategies, model routing, and rate limits designed in from day one — so the OpenAI bill does not quietly become a line item worth renegotiating.
PII redaction, prompt-injection defence, audit logging, and clear data-retention rules. AI features pass security review instead of triggering one.
Model-agnostic architecture means switching between OpenAI, Anthropic, or open-source models is a configuration change — not a rewrite — as the landscape keeps evolving.
No. Most of our AI engagements start with a scoping sprint exactly because “where does this pay off?” is the hard question. In two or three weeks we map your workflows and data, shortlist candidate use cases, and rank them by feasibility and impact. You leave with a defensible plan — whether you implement it with us or not.
LLMs: OpenAI (GPT-4, GPT-4o), Anthropic (Claude 4.x), Google Gemini, plus open-source (Llama, Mistral, Qwen) when cost, privacy, or latency demands it. Classical ML: scikit-learn, XGBoost, PyTorch. Infra: AWS Bedrock, Azure OpenAI, Vertex AI, or self-hosted on your cloud. We default to the boring, well-supported choice and only get fancy when the use case requires it.
Every production AI feature ships with an evaluation harness — offline test set plus online metrics — that we agree on at scoping. Retrieval-augmented generation, grounding, constrained output formats, and clear user-visible confidence cues are standard defaults. If a use case cannot hit a reliable accuracy bar, we say so before you ship it.
Only if you explicitly want it to. We default to data-processing agreements and platform configurations that prohibit training on your inputs (enterprise OpenAI, Bedrock, self-hosted open-source). For sensitive data, we recommend and deploy on-prem or VPC-isolated inference.
