A mandate, but no roadmap
Leadership has committed to AI, yet the team cannot agree on where to start or how to justify the investment.
01 Enterprise AI & FDE
Enterprise AI
From strategy to production, build AI that actually works.
Start from a business result. We scope the use case, prove it, deploy it into the real workflow, and enable the team that owns it. Not an AI strategy deck — a delivery path that runs.
FIT
Who this is for
The hard part of AI is never “can it be built?” It is “which use case, how does it ship, and who owns it after launch?” We answer those three questions with one small, complete delivery loop.
Many pilots, little production
Chatbots and demos exist, but nothing has entered a real workflow. The work stalls at the showcase stage.
Tools bought, adoption stalled
Models, platforms or copilots are licensed, but use-case design, integration and operating norms are missing.
OUTCOMES
What you walk away with
Not a feature list — outcomes that enter the business, get measured and can be owned.
01
A prioritized use-case map
Rank opportunities by business value, feasibility, data readiness and risk. Decide what to do now, later, or never.
02
A pilot inside a real workflow
Not a sandbox demo. Real data, real systems, real users, and metrics agreed before the build starts.
03
A reusable technical foundation
Standardize model access, permissions, logging, evaluation and deployment so the next use case moves faster.
04
An internal team that can take over
Code, documentation, runbooks and training stay with your organization.
DELIVERABLES
What is delivered
Exact scope is tailored per project. Every deliverable has a clear acceptance method.
AI use-case inventory and value matrix
Business goals, baselines and acceptance criteria
Technical feasibility and data-boundary assessment
Working prototype and user-test findings
Production architecture, permissions and logging
Team workshops and an internal rollout playbook
PROCESS
How we deliver
Every stage has a reviewable output, so the project never becomes a long black box.
01
Diagnose
Interview business owners and frontline users. Map workflows, data, systems and constraints. Establish a measurable baseline.
02
Prove
Pick one or two high-leverage use cases and build a working prototype in two weeks. Test it against real tasks and risks.
03
Deploy
Integrate with real systems. Add permissions, exception handling, evaluation and monitoring. Move the pilot into daily work.
04
Enable
Hand over code and documentation. Train product, engineering and business roles. Set up iteration and governance.
USE CASES
Where it shows up
These are typical entry points, not fixed templates. The real scope starts with your workflow.
AI assistant for knowledge work
Turn documents, tickets and chat history into a searchable, citable working assistant.
Sales and support copilots
Prepare account briefs, suggest replies and trigger follow-ups without manual searching and data entry.
Operations automation
Handle reconciliation, reporting and content review — with human fallback where it matters.
Engineering throughput
Bring Codex and other AI coding tools into planning, implementation, testing and review — not just individual experiments.
STACK
Tools and technology
Tools serve the use case. We stay technology-neutral and reuse your existing systems first.
OpenAIAnthropicGeminiDeepSeekQwenDifyn8nCloudflarePostgresMCP
FAQ
Common questions
Something else? Email us. We usually reply within one business day.
How are you different from a traditional AI consultancy?
Consultancies usually deliver recommendations. We deliver a running system and a team that can own it. Every engagement includes a working prototype, production deployment, code and documentation — a report is never the endpoint.
How long does an enterprise AI project take?
Diagnosis and prototype typically take 2–4 weeks. A pilot inside a real workflow usually takes 4–8 weeks. System integration, data readiness and approvals affect the timeline; we provide a staged plan after diagnosis.
How do you handle data security?
We define data boundaries before work starts: what can leave your environment, which models are allowed, how long logs are retained, and who has access. We prefer enterprise APIs, private deployment or de-identification, and keep an audit trail.
Do we have to replace our existing systems?
No. We integrate incrementally with your CRM, ERP, intranet, knowledge base and collaboration tools. Rebuilding an entire system just to add AI is rarely the right first move.
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Start a concrete conversation
Is this use case worth building?
Thirty minutes to lay out the goal, workflow and constraints. We decide the next step together.