Disconnected data
Enterprise systems and machines generate data that is difficult to collect, connect and use.
APE FACTORY / SERVICES / AI & DATA
From data platforms to production-ready AI.
We design, build and operate data platforms, enterprise AI applications and the infrastructure behind them. From real-time telemetry and industrial IoT to RAG, AI agents and model gateways, we connect data, software and infrastructure to deliver working systems.
THE CHALLENGES
From disconnected systems to usable solutions
We start with the problem, not a predetermined technology.
AI pilots without integration
Promising demonstrations need reliable access to knowledge, applications and existing workflows.
Infrastructure decisions
Teams need to choose how models are accessed and where AI workloads should run.
Operational visibility
Security, telemetry, reliability and token costs need attention beyond initial deployment.
WHAT WE DELIVER
Three connected engineering services
Start with the capability your project needs. The services can be combined where appropriate.
Data Platforms & Real-Time Analytics
Design, build and operate platforms connecting enterprise systems, machines and applications.
Typical work
Data architecture, ingestion pipelines, industrial IoT integration, telemetry processing, visualization and platform operations.
Enterprise AI & Agentic Applications
Build AI applications that make enterprise knowledge and data accessible within real workflows.
Typical work
Use-case validation, retrieval architecture, RAG, enterprise search, agents, integration and evaluation.
AI Infrastructure & Gateway Engineering
Engineer the infrastructure connecting applications to AI models and services.
Typical work
Gateway architecture, deployment, configuration, cloud and on-premises infrastructure proofs of concept and integration.
ACROSS THE ARCHITECTURE
Observability, security & cost engineering
AI systems need visibility, controls and an understanding of operating costs beyond initial deployment. Our technical investigations, articles and proofs of concept cover gateway telemetry, monitoring, security, GreenOps and token economics.
We apply this work through targeted assessments, blueprints and scoped engineering—not an implied blanket managed-AI commitment.
SELECTED DELIVERY EXPERIENCE
Built through real engineering engagements
Selected examples are anonymized; no client endorsement or performance metrics are implied.
Enterprise knowledge and AI
An agent-based RAG solution integrating SharePoint, custom AI search and Microsoft Fabric.
Industrial data and visualization
A machine-connected IoT platform using MQTT, Arc and Microsoft Fabric, with custom applications for searching and displaying 3D and telemetry data.
Real-time vehicle telemetry
Development and operation of a platform collecting vehicle telemetry in real time.
AI gateway infrastructure
Multiple gateway deployment and configuration engagements, alongside cloud and on-premises infrastructure proofs of concept.
Our approach
The ape factory Method, applied to AI & Data
Discover → Design → Build → Verify → Operate. An engagement can cover one stage or the complete lifecycle.
- 01
Discover
Understand the use case, data sources, existing systems, constraints and success criteria.
- 02
Design
Define the data, application and infrastructure architecture, including deployment and integration choices.
- 03
Build
Implement and integrate the agreed platform, AI application, gateway or infrastructure.
- 04
Verify
Evaluate against agreed functional, operational, security and cost criteria where relevant.
- 05
Operate
Establish operational responsibilities, monitoring and improvement where included in scope.
OUR ENGINEERING PRACTICE
We use AI in the engineering process—not just in the systems we build.
AI supports our work across research, implementation, testing, documentation and operations. How AI-generated output is reviewed depends on the task and its associated risks. AI assistance does not replace engineering judgment or the need to verify work before relying on it.
Research and architecture
Technical research and exploration of architectural options.
Code and infrastructure
Code generation and refactoring, infrastructure-as-code and configuration.
Testing and debugging
Test generation, debugging and technical investigation.
Documentation and knowledge
Technical documentation and knowledge-management activities.
Internal agents and automation
Agents and automated workflows supporting engineering activities.
Monitoring and operations
Telemetry analysis, monitoring and incident investigation.
HOW WE ENGAGE
Choose a starting point that fits the work
Formats are flexible. Deliverables and responsibilities are defined in the engagement scope.
Expert Sprint · 3–5 days
Assess & Blueprint
Clarify a use case, review an existing system or define an architecture.
Examples
AI use-case assessment, data-platform review, RAG architecture or gateway blueprint.
Proposed outputs: findings, recommendations and prioritized next steps, agreed in scope.
Engineering Sprint · 5–15 days
Prove & Implement
Deliver a bounded technical implementation against agreed acceptance criteria.
Examples
RAG proof of value, gateway deployment, initial data pipeline or observability implementation.
Proposed outputs: working implementation, verification results and documentation, agreed in scope.
Project-based
Build & Integrate
Deliver substantial applications and platforms through defined milestones and iterative engineering.
Examples
Enterprise AI systems, industrial data platforms and real-time telemetry solutions.
Scope, milestones, duration and deliverables agreed individually.
Ongoing
Operate & Improve
Provide ongoing platform engineering and improvement under agreed responsibilities.
Examples
Data-platform operations, monitoring, gateway changes and reliability improvements.
Operational scope and commitments defined with the customer.
