AI pilots are easy. Production AI is where most teams get stuck
AI is being tested, but ROI is unclearMany businesses experiment with AI, but few can connect it to measurable business outcomes that justify further investment.
Internal teams do not have the capacityTechnology teams are already consumed by roadmaps, customer requests, and operational priorities, leaving little bandwidth for AI delivery.
AI pilots never reach real workflowsProofs of concept often demonstrate potential but never become part of the day-to-day systems and processes employees actually use.
The scope gets too big too fastBroad scope, disconnected data, and competing priorities turn promising ideas into long-running projects with limited business impact.
What is the Enterprise AI Assistant?
GROUNDEDAnswers come from your documents contracts, policies, specs with citations on every response.
GOVERNEDPermissions honoured end-to-end. The assistant only sees what each user is allowed to see and every answer is auditable.
MEASUREDKPIs defined before the build starts. Evaluation and monitoring keep quality visible after launch.
Get a free AI assistant discovery sprint
Before you commit, we help you identify the right use case, confirm data readiness, define the KPI, and map what a production assistant could deliver.
For a limited number of teams each quarter, we run the full sprint free of charge.
Claim a free sprint →LIMITED SLOTS AVAILABLE
17 of 25 free sprints filled8 remaining
How the build runs
01
We identify the highest-value use case, validate feasibility, define the KPI, and lock scope before development begins. Key deliverables: use case brief, feasibility confirmation, KPI definition, delivery plan.Timeline: 2-3 days
The pod builds the assistant against your real documents and permissions: retrieval pipeline, prompts, guardrails, and an evaluation harness that scores answer quality on every change.Timeline: 3-4 weeks
Deployment into your environment with SSO, access control, monitoring, and rollout to the first user group. Answers ship with citations from day one.Timeline: 1-2 weeks
We report against the KPI defined in step one, tune from real usage, and hand off runbooks, dashboards, and the evaluation suite to your team.Timeline: 1 week
Meet the delivery pod
Lead AI Architect1 per pod. 8+ years engineering, 3+ years shipping LLM systems in production. Owns technical scope, system design, and delivery accountability from day one to handoff.
AI Engineers1-2 per pod. 5+ years engineering, 2+ years building agents, RAG pipelines, and production integrations. Handle the full build including prompts, evals, APIs, and workflow connections.
Product Designer1 per pod, included when the use case is product-shaped: copilots, assistants, in-product AI surfaces. Owns UX, interaction design, and user-facing output quality.
Specialist StreamsScoped per sprint. Data engineers, MLOps engineers, security specialists, and domain experts are brought in when the use case requires them. Not a fixed cost on every pod.
Success stories in spotlight
All case studies10k+ contract queries answered with citations in the first quarterConstructionEnterprise RAG
"Site teams stopped digging through folders. They ask, they get the clause, they move on."Project Director, Sydney
"Site teams stopped digging through folders. They ask, they get the clause, they move on."Project Director, Sydney50% reduction in support workload with AI-powered document retrievalReal EstateAI Assistant
"We built an assistant that delivers accurate guidance fast, with zero manual work."Director of Operations, Dubai
"We built an assistant that delivers accurate guidance fast, with zero manual work."Director of Operations, Dubai68% faster customer response with an assistant integrated into CRMRetailAgentic AI
"Our support team's response times dropped dramatically. The assistant improved itself over time."Head of Customer Experience, Riyadh
"Our support team's response times dropped dramatically. The assistant improved itself over time."Head of Customer Experience, RiyadhOUR CUSTOMERS
What our customers say
Real feedback from the organisations we build with — what changed, in their words.
All customer stories
Reconciliation that took our team nine days now closes in three — with a full audit trail on every match. It changed how the board sees AI.
Site teams stopped digging through folders. They ask the assistant, they get the clause with a citation, and they move on.
The anomaly models flag outliers the week they appear, not at quarter-end. We stopped two overruns before they hit the P&L.
Our partnerships with
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