Software · Automation · AI
for Modern Enterprises
Software · Automation · AI
for Modern Enterprises
How we engage on AI and intelligent workflow projects — a structured execution model that takes ideas through MVP, into production, and onto your team. Engineering-first, services-driven, no off-the-shelf products being repackaged.
One to two weeks. We sit with your operators, read the runbooks, audit the data, and identify the workflows where AI and automation will pay back fastest. The output is a written discovery brief — what we'd build, why, and the rough cost. You can stop here if it doesn't make sense.
Four to six weeks. We build the smallest end-to-end version that proves the workflow — running on your infrastructure, against your data, with your authentication. Real users get hands-on. We learn what the discovery brief got wrong.
Eight to twelve weeks. The MVP becomes a production system: hardened architecture, security review, monitoring, runbooks, integration with your authentication and access control, and the operational primitives your platform team requires. CI/CD wired in. Testing strategy in place.
Continuous from the start. Your engineers pair with ours during the build. Architecture decision records, runbooks, and training documentation accumulate as we go. By production cut-over, your team can extend, debug, and scale the system without us in the room.
A non-exhaustive list of the workflows we typically take on. Most engagements are a combination of these.
Enterprise data into intelligent knowledge systems — RAG architectures, vector stores, semantic search. AI that can reason over your proprietary information.
Agentic workflows that automate complex business processes — document processing, data extraction, decision-making, multi-step orchestration with human checkpoints.
Conversational voice systems built on the Echo reference architecture or custom-integrated. Customer service, internal helpdesks, scheduling, reception.
Intelligent agents that perceive, reason, and act — LLMs with tool-calling, memory, multi-agent coordination. Built on the Alpha IQ patterns where they fit.
AI deployments that meet enterprise security and regulatory standards — data privacy, model governance, audit trails, GDPR, SOC 2, ISO 27001 alignment.
Take prototypes — yours or ours — through to robust, scalable production: proper architecture, testing, monitoring, CI/CD, and enterprise-grade infrastructure.
Testing strategies for AI systems — prompt regression, hallucination detection, performance benchmarking, continuous evaluation pipelines, golden datasets.
Prompt engineering, model fine-tuning where it pays back, caching strategies, cost monitoring, and resource utilisation tuning for production deployments.
Five rules of thumb that have held up across 17 years of enterprise engagements.
If your team can't operate it without our help, we built the wrong thing. Every engagement is scoped around what your engineers will inherit.
We embed AI into existing systems where it removes real friction. We don't sell "an AI" — we deliver workflows that happen to use models when models are the right answer.
Most failed AI projects work fine in the lab and fall over the moment real users meet real edge cases. We design for production from week one and validate against real load.
Every system we ship surfaces what it's doing and what it costs. Token spend, tool calls, latency budgets — visible to your team, not hidden behind opaque vendor billing.
Autonomous AI is a long-term direction, not a starting point. Every workflow we ship has a human checkpoint where one belongs — and we engineer those checkpoints to be productive, not friction.
A short conversation, then a one-to-two week structured discovery sprint. You walk away with a written brief — what we'd build, what it would cost, and whether it makes sense to continue. No commitment beyond the discovery.