AI Deployment. Training with Integrity.
Built for Impact.

We identify the highest-cost manual processes in your operations and replace them with production AI systems — integrated into your existing stack, governed from day one, and handed off with the documentation your team needs to run them without us.

• Operations audit identifying highest-cost manual workflows
• Production AI systems integrated into your existing tools and infrastructure — Salesforce, HubSpot, internal platforms, custom APIs, Amazon AWS
• Multi-agent architectures with Redis Streams orchestration, PostgreSQL audit trails, and observability dashboards
• LLM integration with structured routing — directing tasks to the right model based on language, data sensitivity, and task type
• Cost governance, token budget management, and graceful degradation built in
• Team handoff with runbooks, operational training, and monitoring

Building AI is the easy part. Governing it — so that it runs reliably, handles sensitive data responsibly, satisfies regulators, and doesn't fail silently — is the engineering that most teams skip. We don't.

We published the Agent Engineering Standard — a 13-category governance specification for production AI agent systems, developed from direct experience with systems that broke in production. Every category traces back to a real failure mode: token budgets that exploded, error taxonomies that didn't exist, audit trails that couldn't answer the regulator's question. We've developed AI Use Policies adopted by organisations working in M&E, peacebuilding, and financial services — covering data classification, human-in-the-loop verification, incident response, and donor disclosure.

• Agent Engineering Standard implementation — mapping the 13 categories to your specific systems and regulatory environment
• AI Use Policies for your organisation — data classification frameworks (Red/Amber/Green), prohibited uses, verification requirements, incident response protocols
• Compliance mapping for regulatory regimes: EU AI Act (Articles 9–17, 26), MiFID II, GDPR, CBB regulations, and donor-specific requirements (USAID, FCDO, EU, Netherlands MFA)
• AI governance audits of existing systems — identifying gaps between your current controls and what production and compliance require
• Engineering standards for supporting infrastructure: Solidity smart contracts, Terraform/IaC, CI/CD pipelines — each following the same three-layer enforcement model

Who this is for: Organisations deploying AI, handling sensitive data, or reporting to institutional stakeholders who ask how AI is governed. If your board, regulator, or donor will eventually ask “how do you control your AI systems?” — this is how you have the answer ready before they ask.

Generic AI training teaches your team to write prompts. Our training teaches them to use AI responsibly in environments where a mistake has consequences — where the data belongs to refugees, where the analysis informs a peace negotiation, where the report goes to a donor who expects every number to have a source.

We deliver live training workshops supported by our proprietary AI Training Sandbox — a multi-lingual (Arabic, French, English) platform designed for hands-on practice using realistic data and structured exercises. Crucially, the competencies developed in our safe environment are entirely tool-agnostic. Whether your organization relies on Google Gemini, Anthropic Claude, OpenAI's ChatGPT, other tools or a mix of platforms, your team will gain universally applicable AI skills. We tailor these programs specifically for monitoring and evaluation, peacebuilding, civil society, humanitarian, and development organizations working with sensitive data and vulnerable populations.

What makes our training different:

• Domain-specific, not generic. Every exercise uses data and scenarios from the trainee's actual work context — not abstract examples
• “Thought partner, not replacement.” We teach AI as a tool that supports professional judgement, not one that substitutes for it. The framing matters: teams that feel AI threatens their expertise will resist it. Teams that see AI as a critical enabler will adopt it.
• Governance built into the curriculum. Module 0 teaches data classification, AI use policy, and ethics (particularly with vulnerable populations) BEFORE any AI tool is introduced. Every subsequent module reinforces verification habits and responsible use.
• Multilingual by design. The platform processes Arabic through a specialised Arabic language model (Falcon-H1 Arabic) while using Claude for structured reasoning and French/English tasks. Trainees learn which model to use for which task.