Enterprise AI
The foundations that make AI useful inside a business.
Knowledge systems, retrieval, integrations, evaluation and secure infrastructure - the engineering layer underneath every reliable AI application.
Applications
- AI Customer Support Agents
- Agentic Commerce
- AI Workflow Automation
- Predictive systems
Enterprise AI foundation
- Knowledge systems
- Retrieval
- Software integrations
- Evaluation
- Security
- Production infrastructure
Why enterprise AI is hard
The model is rarely the difficult part
- 01
Scattered knowledge
Policies, procedures and project history live in many systems with different formats and owners.
- 02
Permissions
People should only see answers drawn from information they are already allowed to access.
- 03
Quality you can trust
Without systematic evaluation, it is hard to know whether answers are accurate enough to rely on.
- 04
Path to production
Prototypes need monitoring, cost control, security review and integration before teams can depend on them.
Capabilities
Engineering for AI in production
We design these capabilities around your existing data, tools and controls.
Enterprise knowledge systems
Ingest, structure and index documents and records so they can be searched and reasoned over.
Retrieval-augmented generation
Ground model responses in retrieved sources, with citations, where accuracy and traceability matter.
Internal AI assistants
Assistants for teams such as operations, sales or HR, scoped to their knowledge and tools.
Data and software integrations
Connect AI to databases, APIs and business applications through well-defined, permissioned interfaces.
Evaluation and governance
Test sets, quality metrics, review workflows and usage policies that make behaviour measurable.
Security and access control
Respect existing permissions, protect sensitive data and keep an audit trail of what the system did.
Retrieval-augmented generation
How a grounded answer is produced
A simplified view of an internal assistant answering a policy question.
Conceptual pipeline. Components vary with data, scale and requirements.
Flow
Illustrative01Employee asks a question
“What is our approval limit for supplier contracts?”
assistant UI
02Check permissions
The request is scoped to documents this person is allowed to see.
identity provider
03Retrieve relevant sources
Search indexed policies and procedures for the most relevant passages.
document index
04Answer with citations
The model answers from those passages and links to the source documents.
05Log for evaluation
Question, sources and answer are recorded for quality review and improvement.
evaluation store
Deployment considerations
Decisions we work through with you
Enterprise deployments involve trade-offs. We make them explicit early.
Hosting and data residency
Choose between managed model APIs, private cloud deployments or self-hosted models based on your requirements.
Model selection
Match models to tasks by quality, latency and cost - and keep the option to change them.
Monitoring and cost
Track quality, usage and spend in production, with alerts when behaviour drifts.
Change management
Plan rollout, training and feedback channels so people can rely on the system confidently.
Implementation approach
How we would approach it
Every engagement is shaped by your systems and constraints. A typical path looks like this.
- 01
Assess readiness
Review data sources, access controls and candidate use cases to find a sound starting point.
- 02
Design the architecture
Define ingestion, retrieval, model, integration and evaluation components for the first use case.
- 03
Build with evaluation
Develop alongside a test set so quality is measured from the first iteration.
- 04
Operate and extend
Deploy with monitoring, then reuse the foundation for further use cases.
Have a business problem AI could solve?
Let’s understand the challenge and explore what a useful solution could look like.
Or email info@skarp.global