Engagement Models.
Clear, structured ways to work with us. No open-ended consulting traps.
Workflow Audit
Investment
Fixed Fee
Timeline
2 Weeks
A deep-dive analysis of your current operations to identify exactly where AI and automation will drive the highest ROI.
What's included:
- Current state workflow mapping
- Bottleneck and inefficiency identification
- AI feasibility and risk assessment
- Future state architecture design
- Fixed-price proposal for the Build phase
Workflow Build
Investment
Project Based
Timeline
4-12 Weeks
The engineering phase. We build, test and deploy the custom AI systems and automations designed in the Audit phase.
What's included:
- Custom AI Agent development
- RAG pipeline integration with your data
- API integrations with existing SaaS
- User interface / internal tool development
- Testing, evaluation and team handover
Managed Evolution
Investment
Monthly Retainer
Timeline
Continuous
AI models change weekly. We maintain, monitor and continuously improve your systems so they never degrade in production.
What's included:
- Uptime and error monitoring
- Prompt optimisation and tweaking
- Model upgrades (e.g., GPT-4 to GPT-5)
- Monthly performance reporting
- Break-fix support and maintenance
Fractional AI Engineering
Investment
Retainer
Timeline
Flexible
Need an AI engineering team but not ready to hire full-time? We embed with your team to lead AI strategy and execution.
What's included:
- Dedicated engineering hours
- Technical leadership and architecture
- Direct Slack/Teams integration
- Code reviews and team upskilling
- Strategic roadmap planning
The Philosophy
Why production-grade AI is never "done".
Most software development follows a predictable path: build, deploy, hand over. But AI is not traditional software. It is non-deterministic, highly dependent on evolving data and built on top of foundational models that change weekly.
If you treat an AI integration as a one-off project, it will inevitably degrade. Prompts that worked perfectly on GPT-4 might break on GPT-4.5. Edge cases in your data will cause unexpected hallucinations. APIs will deprecate.
Consultancies deliver a slide deck, hand over the keys and disappear. We don't just build the engine; we act as the pit crew.
What Managed Evolution entails:
Continuous Evaluation & Telemetry
We don't just wait for things to break. We use tools like LangSmith to monitor token usage, latency, and output quality in real-time, catching hallucinations or degraded responses before they impact your operations.
Prompt & Pipeline Optimization
As your team uses the system, we analyse the edge cases and continuously refine the prompts, RAG retrieval strategies and agent logic to make the outputs sharper and more accurate over time.
Seamless Model Upgrades
When a faster, cheaper, or smarter model is released (e.g., moving from Anthropic's Claude 3.5 to Claude 4), we test, benchmark and migrate your workflows without you having to lift a finger.
The Managed Partner Promise
To claim a system is "production-grade" means it is resilient, monitored and supported. By engaging in Managed Evolution, you aren't just buying code—you are partnering with a team that takes ongoing accountability for the ROI and reliability of your AI workflows.