Model engineering & evaluation
Model development, adaptation, inference, and evaluation engineered against a defined workload—not a benchmark in isolation.
- Model development
- Adaptation & post-training
- Evals & inference
Independent software engineering
We engineerthe intelligencebetween systems.
AI models, agent infrastructure, robotics, embedded systems, and custom software—engineered for the real world.
INTELLIGENCE STACK / 01
Company
Cogitave is a software engineering and consulting company. We work at the systems level—where models, software, machines, data, and people meet.
Capabilities
Six connected domains. One engineering practice.
Capabilities
Agent runtimes & automation
Durable agents that coordinate tools, state, memory, APIs, and human decisions through explicit runtime boundaries.
- Runtime & orchestration
- Tool and identity boundaries
- Human control
Data, knowledge & integration
Data products, retrieval systems, knowledge surfaces, and interfaces that give software and agents trustworthy context.
- Data products & pipelines
- Knowledge & retrieval
- Enterprise APIs
Product & platform engineering
Digital products, internal platforms, and custom software designed to remain operable, observable, and ready to evolve.
- Digital products
- Developer platforms
- Application modernization
Robotics, embedded & edge
Real-time control, sensing, embedded software, and edge intelligence engineered across the complete path to operation.
- Control & perception
- Embedded and real-time
- Fleet & edge operations
Security & cyber resilience
Identity, application, cloud, AI, and operational controls designed into the system together with detection and recovery.
- Zero Trust & identity
- AI and application security
- Incident readiness
Solutions
Engineering around the systems your business already runs.
From a constrained first assessment to systems that remain observable, operable, and ready to evolve.
- 01Assess
- 02Architect
- 03Integrate
- 04Operate
01S / 01
Intelligence with operational boundaries
Enterprise AI enablement
AI applications, model systems, and agent workflows designed to work across existing cloud, data, and business environments.
- Model gateways and data pathways
- Agent and copilot workflows
- Evaluation, observability, and governance
02S / 02
Context that systems can trust
Data & knowledge platforms
Data products, retrieval systems, knowledge surfaces, and operational pipelines that make information usable by people, software, and agents.
- Data products and pipelines
- Knowledge and retrieval systems
- Governance, lineage, and quality
03S / 03
Core systems that move as one
Enterprise platform integration
Extensions, APIs, data flows, and automation around the platforms that already carry critical business operations.
- Integration architecture and APIs
- Application extensions
- Workflow and data automation
04S / 04
Products that can keep changing
Digital product & platform engineering
Customer-facing products, internal platforms, and developer systems engineered from product intent through reliable operation.
- Product discovery and experience
- Software and platform architecture
- Delivery, observability, and evolution
05S / 05
Change without operational drift
Cloud & application modernization
Application and platform foundations modernized with a clear migration path, measurable reliability, and continuity for operators.
- Architecture and readiness assessment
- Application and data modernization
- Platform engineering and operations
06S / 06
Software that reaches the physical world
Connected products & robotics
Embedded, edge, sensing, and robotic systems engineered from real-time control through fleet visibility and operation.
- Edge and embedded architecture
- Control, sensing, and real-time software
- Telemetry and fleet operations
07S / 07
Trust made enforceable
Security engineering & cyber resilience
Threat-led architecture, identity, application, cloud, AI, and operational controls engineered into the systems the business depends on.
- Zero Trust and identity architecture
- Application, cloud, and AI security
- Detection, incident readiness, and resilience
Enterprise ecosystems
Platform-aware engineering, shaped around your architecture and delivery constraints.
Microsoft
Microsoft Foundry · Azure · Fabric · Dynamics 365 · Power Platform
AI systems, data estates, business applications, identity, and cloud modernization
AWS
Bedrock & AgentCore · SageMaker AI · EKS · Lambda · Data services
Agent and model systems, cloud-native platforms, data foundations, and modernization
SAP
BTP · S/4HANA · Integration Suite · Datasphere
Clean-core extensions, integration, workflows, data products, and intelligent applications
Oracle
OCI · Fusion · Database · Oracle Integration
Cloud, enterprise applications, data, integration, and lifecycle engineering
Open data & AI
Kubernetes · PostgreSQL · Kafka · Open model stacks
Portable runtimes, governed data pathways, APIs, and purpose-built AI platforms
Private, edge & sovereign
On-premise · Air-gapped · Edge fleets · Embedded
Private infrastructure, connected machines, offline authority, and constrained operations
Platform names identify technical scope only; they do not imply certification or formal partner status.
Engineering standard
Quality is not
a finishing pass.
It is the architecture: measurable behaviour, explicit boundaries, clear decisions, and software the next engineer can understand.
- 01
Evidence over intuition
We define how a system will be evaluated before treating its behaviour as dependable.
EVALSTESTS - 02
Legibility by design
Interfaces, decisions, failure modes, and ownership stay understandable to the people operating the system.
CLARITYCONTROL - 03
Simple where possible
We choose the least complex architecture that meets the real constraint—then make its trade-offs explicit.
FOCUSRIGOR - 04
Built for the long haul
Observability, documentation, and change are part of the first design, not a clean-up phase.
OPERATEEVOLVE
Method
From first principles
to production.
A disciplined path that turns uncertainty into evidence, evidence into an engineered system, and production behaviour into the next decision.
- 01System definition
Frame
Make the problem boundary explicit before choosing models, software, or hardware.
Outcome and constraintsInterfaces and failure modesOutputSystem brief - 02Critical evidence
De-risk
Test the hardest assumptions early with the smallest experiment that can produce useful evidence.
Prototype or simulationEvals and acceptance criteriaOutputDecision-grade evidence - 03Integrated build
Engineer
Build the complete system and make every boundary testable, observable, and understandable.
Models, software, and hardwareTests, telemetry, and safeguardsOutputProduction system - 04Measured evolution
Operate
Use real behaviour—not assumptions—to maintain, learn from, and improve the system.
Production behaviourOwnership, runbooks, and iterationOutputOperating feedback
Our principles
Durable.
Observable.
Composable.
We engineer systems that earn trust: predictable, transparent, and built to change. Control, reliability, and clarity are not afterthoughts—they are the foundation.
Start a conversation
Bring us the
hard problem.
Models, agents, robotics, embedded systems, and custom software—engineered as one coherent system.
