AI Development & Consulting
AI strategy, LLM integration, RAG systems, and machine learning — built to production standards with evaluation and guardrails, not demos.
- Honest feasibility assessment before any build commitment
- Evaluation harness so quality changes are measurable
- Defined failure and escalation behaviour, not silent errors
- Model-agnostic architecture that survives provider changes
- OpenAI
- Claude
- Google Gemini
- LangChain
- Vector databases
- Python
- TypeScript
- Eval-first
- Quality measured, not assumed
- Multi-model
- Provider-agnostic build
- 2-4 wk
- Typical prototype
What AI development & consulting covers
The gap between an impressive AI demo and a system you can put in front of customers is large, and it is where most AI projects stall. A demo needs to work once. A production system needs to work reliably, fail safely, cost predictably, and be measurable.
Feasibility before build
We start by asking whether AI is the right tool. A meaningful share of requests we receive are better solved with conventional software: a search index, a rules engine, or fixing a broken process. Recommending that costs us revenue and saves you a great deal more, so we do it.
Evaluation is the core discipline
Without an evaluation harness you cannot tell whether a prompt change improved things or quietly broke a category of queries. We build evaluation sets from real examples early, and use them to gate changes. This is the single practice that most separates AI systems that hold up from ones that degrade unnoticed.
Guardrails and failure behaviour
Production systems need defined behaviour when the model is uncertain, when retrieval returns nothing relevant, and when a user attempts prompt injection. We design escalation paths and refusal behaviour deliberately, and log interactions so problems can be diagnosed.
Model-agnostic architecture
The model landscape shifts quickly. We build behind an abstraction so you can move between providers as capability and pricing change, rather than rewriting your application each time.
Where AI development & consulting makes the difference
The challenges we hear most often — and how we address each one.
AI pilots never reach production
We scope use cases around measurable outcomes and plan deployment from the start.
Unclear where AI would actually help
An opportunity review ranks use cases by value, feasibility, and data readiness.
Concerns about data privacy
We design for data residency, access control, and private model options.
Model answers are unreliable
Retrieval, guardrails, and evaluation suites keep outputs grounded and testable.
Our AI development & consulting services
Each service has its own page covering scope, process, deliverables, and pricing tiers.
Predictive Analytics
Forecasting and prediction models — demand, churn, risk, and maintenance — built on your data and delivered into the tools your teams already use.
Learn moreAI Workflow Automation
Document processing, triage, and internal workflow automation using AI where it fits and conventional logic where it does not.
Learn moreMachine Learning Development
Predictive models, classification, and computer vision built with proper validation, monitoring, and retraining pipelines.
Learn moreRAG & Knowledge Systems
Retrieval-augmented generation over your own documentation, policies, and product data, with citations and measured retrieval quality.
Learn moreLLM Integration
Production integration of OpenAI, Claude, and Gemini into your products and workflows, behind a provider-agnostic abstraction.
Learn moreAI Consulting & Strategy
Independent assessment of where AI will genuinely pay in your business, with a prioritised roadmap and honest feasibility findings.
Learn moreAI Chatbots & Agents
Conversational AI chatbots and autonomous AI agents for support and sales.
Learn moreWhat we deliver
Key features and capabilities of our AI development & consulting work.
AI strategy & feasibility
Use case assessment, data readiness review, and honest build-or-do-not-build advice.
LLM integration
Production integration with OpenAI, Claude, or Gemini behind a provider abstraction.
RAG systems
Retrieval over your own content with chunking, embedding, and relevance tuning.
Evaluation harness
Test sets built from real queries, used to gate every change.
Guardrails & monitoring
Refusal behaviour, escalation paths, logging, and cost and quality monitoring.
How the engagement runs
A clear sequence with a visible output at every step — so you always know where the project stands.
- 01
Discovery & feasibility
We assess the use case, data readiness, and whether AI is genuinely the right tool before proposing a build.
- 02
Prototype & evaluation
A working prototype measured against defined accuracy and cost criteria, so the decision to proceed is evidence-based.
- 03
Production build
Hardening, guardrails, monitoring, evaluation harness, and integration with your systems.
- 04
Monitor & improve
Ongoing quality monitoring, prompt and retrieval tuning, and model updates as the landscape changes.
Technologies & tools
Chosen per project for fit, maintainability, and your existing stack.
- OpenAI
- Claude
- Google Gemini
- LangChain
- Vector databases
- Python
- TypeScript
Industry applications
Sectors where we most often apply this service.
- E-Commerce & Retail
- SaaS & Technology
- Healthcare
- Real Estate
- Finance & FinTech
- Education
- Travel & Hospitality
- Professional Services
What's included at each level
Every engagement is scoped to your needs; these tiers show how scope typically grows.
| What's included | Starter | Growth | Enterprise |
|---|---|---|---|
| Feasibility & use case assessment | Included | Included | Included |
| Working prototype | Included | Included | Included |
| Evaluation harness & test sets | Not included | Included | Included |
| Production build & guardrails | Not included | Included | Included |
| Monitoring & cost dashboards | Not included | Not included | Included |
| Ongoing tuning & model updates | Not included | Not included | Included |
AI Development & Consulting: common questions
Is AI actually right for our problem?
Often, but not always. Deterministic problems with clear rules are usually better served by conventional software, which is cheaper and more predictable. We assess this first and will recommend against a build when that is the honest answer.
How do you handle hallucination?
Grounding responses in retrieved source material, citing sources so answers are checkable, defining refusal behaviour for low-confidence cases, and evaluating against test sets. You reduce and bound the risk; you do not eliminate it, and any vendor claiming otherwise is overselling.
What about our data privacy?
We design around your requirements: provider agreements with no training on your data, self-hosted models where regulation demands it, and PII redaction before anything leaves your environment. The architecture follows your compliance position.
What does it cost to run?
Token cost per interaction depends on model, context size, and volume. We model this during the prototype so you see running cost at your expected scale before committing to production.
Explore related services
Ready to talk about AI development & consulting?
Tell us what you need. We reply within one business day with questions or a call slot — and a fixed, written proposal after.