AI readiness and strategy
Assess your current systems, data maturity, and business goals to define an AI roadmap that is practical and measurable.
AI Services
AI Agents for execution-heavy B2B operations
We design and deploy AI agents that handle repetitive coordination, document-heavy workflows, and cross-system tasks across finance, procurement, operations, and customer service.
- Agent-assisted process execution with human approval controls
- Integration with ERP, CRM, and internal knowledge sources
- Clear KPIs: cycle-time reduction, error-rate reduction, and service-level improvement
RAG + Orchestration
Grounded · Auditable · Controlled
Enterprise-ready
Finance · Procurement · Ops
How it works
Where AI actually belongs in your operation
AI agents sit between your existing systems and your teams. They read, reason, act — and hand off to humans when it matters.
Data sources
ERP systems
CRM platforms
Documents & SOPs
Email & comms
Internal knowledge
AI agent layer
Orchestration— breaks tasks into executable steps
RAG retrieval— grounds answers in your actual documents
Tool calls— reads and writes to your connected systems
Approval routing— escalates based on risk and confidence
Audit logging— every action is traceable
Outputs
Automated actions
Human review queue
Escalation alerts
Structured reports
Full audit trail
How we help companies implement AI
Our delivery model combines business alignment, technical integration, and production-safe execution.
ERP AI integration
Embed AI workflows into ERP operations for forecasting, exception handling, and intelligent process optimization.
Software + agent implementation
Build AI-enabled applications and agent orchestration layers that automate repetitive operations across teams.
Governance and production quality
Ship safely with human review, security controls, observability, and reliability standards aligned to enterprise environments.
Delivery roadmap
How a deployment actually unfolds
Five phases from first conversation to scaled production. Each phase has defined outputs before we move forward.
- 1
Discovery
Discovery and use-case prioritization
Map your operations to identify where AI creates measurable value — not just where it could theoretically apply.
↳ Prioritized use-case list - 2
Pilot
Pilot architecture and workflow definition
Design the agent architecture, tool connections, and approval flows for the highest-priority use case.
↳ Pilot architecture doc - 3
Build
Controlled implementation and integration
Deploy into a controlled environment with real data, measure performance against baseline KPIs.
↳ Working integration - 4
Harden
Production hardening and governance rollout
Apply security, observability, fallback paths, and enterprise governance before production access.
↳ Production-ready system - 5
Scale
Performance optimization and scale-up
Expand to additional use cases and teams with a proven pattern, monitoring, and operational runbooks.
↳ Scaled deployment
AI Agents and RAG
From chat experiments to operationally reliable AI systems
Retrieval-Augmented Generation (RAG) gives AI agents dependable access to your policies, contracts, product data, and operating procedures. This means fewer hallucinations and more auditable responses in B2B environments where accuracy matters.
We design agent workflows where RAG powers grounded reasoning, while approvals and escalation rules keep mission-critical decisions under control.
RAG — how agents get grounded answers
Policies
Contracts
SOPs
Product data
Embedding model— converts text to vectors
Vector database— indexed knowledge store
Top-K retrieval
relevant chunks
+
Agent query
the actual task
Language model (LLM)— generates answer with retrieved context
Grounded response— cited, auditable, source-traceable
Agent workflow — from trigger to output
Business trigger
Invoice received, exception flagged, approval requested…
Agent orchestrator
Understands task → selects tools → applies RAG context → constructs response
Tool calls
ERP API, CRM lookup, document search, email / notifications
Approval gate
Low-risk → auto-execute · High-stakes → human review queue
Output
Action executed · record updated · audit log written · team notified
AI capability building
Prompt engineering training for business and delivery teams
We run practical AI training programs that teach your teams how to design high-quality prompts, structure reusable playbooks, and measure output quality against business KPIs.
Role-based tracks
Separate training paths for leadership, operations, and technical teams.
Prompt patterns
Frameworks for extraction, summarization, SOP execution, and decision support.
Governance-first usage
Risk controls, response validation, and prompt lifecycle management standards.
Related services
Connect AI agents to the systems that run the business.
AI services perform best when they are grounded in data, ERP records, and custom software that can execute the workflow.
AI and dataAI and Data SolutionsShape AI use cases, data foundations, and governance controls before production rollout.ERPERP ImplementationConnect AI agents to reliable operational records, workflows, and approval structures.SoftwareCustom Software DevelopmentBuild the applications, APIs, and orchestration layers that AI agents need to execute work.
Related insights
Further reading for practical AI adoption.
These resources support the path from first AI use case to production-ready operations.
Free guidesAI adoption and promptsDownload Leeway guides for AI use cases, team workflows, and prompt quality.InsightAI in ERPA practical article on finding first AI use cases with measurable operational value.Case studyERP and web ecosystemA delivery example where integrated systems improved visibility and growth execution.
Production safeguards
Vibe-code review and hardening before production
We review and harden AI-generated implementations before go-live, turning fast prototype code into production-grade systems with clear reliability, security, and maintainability standards for B2B operations.
This service is designed for teams using vibe coding to accelerate delivery, but who still need enterprise-level control over architecture, compliance posture, observability, and release quality.
Security and dependency risk checks
Code quality and maintainability review
Performance and scalability assessment
Production deployment readiness validation
Prompt and model behavior traceability checks
Operational runbooks, fallback paths, and incident playbooks