AI Agents Built for Production, Not Demos
We engineer autonomous AI agents that integrate with your systems, follow governance rules, and deliver measurable business outcomes — with architecture, monitoring, and long-term ownership from day one.
Business outcome: Reduce manual operational work by 60–80% with agents that run reliably in production.
60–80%
Operational tasks automatable
<200ms
Tool-call latency target
8–12 wks
Typical single-workflow deployment
End-to-end delivery
One-stop solution for AI Agents
We support every stage of the service journey, from early ideation and evaluation to prototyping, production launch, and ongoing optimization. The goal is simple: improve business metrics with systems your team can trust and operate.
Ideation
Shape the opportunity, define the business metric, and decide what is worth building.
Evaluation
Assess data, integrations, risk, and feasibility before committing engineering time.
Prototype
Validate the workflow with real users, real data, and measurable acceptance criteria.
Launch
Ship with security review, monitoring, documentation, and ownership in place.
Production focus
Strategy, build, launch, and long-term ownership in one delivery model.
Why it matters
Enterprise buyers need production systems — not pilots that stall after a quarter. We engineer with architecture review, security controls, monitoring, and documentation your team can operate.
Aqeeq approach
Every engagement starts with a business outcome, maps to a workflow architecture, and ships with CI/CD, evaluation frameworks, and observability from day one.
Unreliable tool calling
Brittle API integrations crash workflows without validated schemas and retries.
No human oversight
High-stakes actions need approval gates, not optional add-ons.
What we handle
A compact delivery stack built around measurable outcomes, not disconnected tasks.
Multi-Agent Orchestration
LangGraph, CrewAI, or custom topologies with clear handoffs.
MCP Tool Integration
Standardized, secure connectivity to internal systems.
Memory & Context
Session state and knowledge retrieval with permission-aware access.
RAG Knowledge Systems
Hybrid retrieval with reranking for domain accuracy.
Evaluation Frameworks
Golden datasets and regression testing on every change.
Enterprise Governance
Audit logs, RBAC, prompt versioning, and compliance reporting.
How delivery moves
A clear path from idea to operated system, with decisions and evidence at every stage.
Discover
Discovery
Map workflows, integrations, and success metrics.
Design
Architecture
Design system contracts, security model, and infrastructure.
Build
Prototype
Validate against real data with stakeholder review.
Launch
Engineering
Production build with monitoring and documentation.
Improve
Optimization
Cost tuning, accuracy improvement, and expansion.
Technology fit
Frequently asked questions
A focused agent for a single workflow typically takes 8–12 weeks from discovery to production. Multi-agent systems may take 14–20 weeks. We deliver a working prototype within the first 4 weeks.
Yes. We design versioned tool contracts with validation, retries, and fallback paths for Salesforce, SAP, ServiceNow, HubSpot, and custom APIs.
Ready to move from pilot to production?
Start with a conversation grounded in engineering reality.