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GenAI & Agentic Systems

RAG-powered knowledge platforms to autonomous multi-agent workflows — built to work reliably in production enterprise environments.

Talk to our GenAI team View case studies

Who this CoE is for

Enterprise teams in transition

Moving GenAI out of pilots and demos into reliable, governed production systems.

AI-native product builders

Companies building end-to-end AI-native platforms where model quality is just the starting point.

Workflow automation teams

Organizations replacing manual, multi-step operations with autonomous agents and tool orchestration.

Regulated organizations

Environments where auditability, governance, and controlled AI behavior are non-negotiable.

What this CoE helps you achieve

RAG in production

Reliable knowledge access across documents, systems, and teams — with retrieval that works at enterprise scale.

Agentic workflows

Multi-step tasks executed by LLM-guided agents with tools, memory, and guardrails — observable from first prompt to final API call.

Multi-agent orchestration

Specialised agents coordinating through typed contracts: parallel work, handoffs, retries, and human approval without fragile prompt chains.

Cost & quality control

Token budgets, routing, caching, and evaluation harnesses so quality holds as traffic and model spend grow.

Production operability

Runbooks, tracing, incident response, and release discipline so systems survive real users and organisational change.

What this CoE focuses on

Production-grade GenAI and agentic systems that work in enterprise environments — from knowledge retrieval to autonomous workflow execution.

From demos to production

Deploying with reliability and governance — not just a working prototype.

Cost control at scale

Serving thousands of users without runaway inference spend.

Accuracy, auditability, compliance

Regulated environments need full traceability on every AI action.

Multi-agent with human oversight

Coordinating agents while keeping humans in the loop at critical steps.

Why it matters

Most GenAI prototypes fail between demo and deployment. Agentic systems add new complexity — autonomous decisions, tool calling, and multi-step workflows that must be reliable, governable, and cost-effective.

  • Accuracy & Trust

    How do you measure and maintain accuracy systematically — for both retrieval and agent decisions?

  • Governance

    How do you audit AI and agent actions in regulated environments with full traceability?

  • Scale & Cost

    How do you serve 10K+ users without runaway inference bills?

  • Reliability

    How do you handle failures, edge cases, and ensure safe autonomous behavior?

Agentic Systems — The Next Frontier

Agents can automate complex, multi-step workflows that traditional automation can't handle. But they demand a higher level of engineering discipline.

Reliability

Deterministic execution, validation gates, and failure recovery at every step.

Control

Human-in-the-loop approval, escalation paths, and rollback capabilities.

Safety

Tool access controls, decision logging, and full audit trails.

Observability

Cost tracking, performance monitoring, and quality metrics — continuously.

BeeHyv has built and operated these systems at scale. Our frameworks encode lessons from production deployments spanning RAG platforms, agentic workflows, and AI-native products.

Engineering Frameworks & Assets

Production-grade data pipelines and governance patterns that make analytics and AI systems reliable at scale.

RAG Framework Library

Knowledge retrieval at enterprise scale

Production-grade data pipelines and governance patterns that make analytics and AI systems reliable at scale.

Core capabilities

Engineering assets

Multi-source integration with inherited RBAC across documents, collaboration tools, and enterprise systems
Hybrid retrieval — semantic search, keyword search, and knowledge graph–based enrichment combined
Intelligent context management for large documents, video transcripts, and structured content
Evaluation-led quality assurance using industry-standard frameworks
Horizontal scalability with production-grade performance and reliability
Connector framework (doc stores, ticketing, media)
Configurable RAG library (chunking, retrieval, re-ranking)
Evaluation harnesses and golden datasets
Knowledge graph enrichment pipeline

Agentic Workflow & Automation Framework

Multi-agent orchestration with real governance

Agents coordinate tools across systems — with clear contracts, observability, and human control where it matters.

Core capabilities

Engineering assets

DAG execution with branching, parallelism, retries, and conditional paths
Multi-agent coordination with typed handoffs and shared context boundaries
Human-in-the-loop approvals with pause, notify, and full audit of outcomes
Tools exposed as services with connectors for email, Slack, databases, and custom APIs
ClickHouse-backed analytics on latency, cost, and failures per run and per agent
Workflow test harness and golden trace regression
Policy and guardrail hooks for regulated environments
Pre-built integration patterns for enterprise IAM and ticketing
Built-in testing against unsafe tool calls and schema drift

TalkToYourApp — Conversational API Access

Any system, conversationally accessible

Deterministic execution against your real APIs — conversational UX without rewriting backends.

Core capabilities

Engineering assets

Swagger/OpenAPI to generated callable tools with schema validation before every call
Multi-channel deployment: web, mobile embed, and messaging surfaces from one integration
Sensitive flows with confirmations, policy blocks, and reasoning logs
Deterministic execution paths with rollback-aware patterns where supported
Low-latency options for constrained networks and multilingual UX where needed
Writes to system-of-record with operational monitoring and audit exports
Operator dashboards for conversation quality and failure triage

How we deliver

CoE-Anchored Squad

Every squad is backed by shared CoE frameworks, libraries, and governance patterns — ensuring consistency across engagements.

AI/ML Engineers

RAG pipelines, embeddings, vector search, model integration

Agentic Systems Engineers

Multi-agent orchestration, tool integration, workflow automation

Backend Engineers

API integrations, system connectivity, performance

QA Engineers

Evaluation frameworks, continuous testing, quality metrics

Product/Domain Expert

Use-case validation, accuracy benchmarking, user feedback

Delivery Approach

1

Assess

Use-case validation, data readiness checks, and target quality metrics definition

2

Build

RAG pipelines, agent workflows, connectors, and system integrations

3

Evaluate

Continuous quality measurement using RAGAS / DeepEval and accuracy benchmarks

4

Harden

Governance, audit trails, RBAC, safety guardrails, and cost controls

5

Optimize

Performance tuning, inference cost reduction, and scale testing

6

Transfer or Operate

Based on team readiness, ownership model, and long-term needs

Ownership & Long-Term Control

Enterprises are not locked into commercial platforms or black-box systems. Frameworks and source code can be licensed. Deployments run inside your own infrastructure.

License the underlying frameworks and source code

Deploy inside your cloud, VPC, on-prem, or air-gapped environment

No mandatory SaaS lock-in or usage-based rent

Internal teams can operate independently over time

Technology Stack

Representative work

See how we deliver

50K+

Enterprise Knowledge Access Platform

Regulated industry required reliable knowledge access across 50K+ documents — hybrid retrieval, multi-level validation, and full audit trails for compliance.

70% reduction

Multi-Agent Workflow Automation

Operations teams automated 12 distinct workflows across 8 enterprise systems — with human approval gates and full rollback capability.

70% reduction

6 months

AI Product — Concept to Production

Tech startup needed a GenAI-powered analytics platform built end-to-end — RAG-based insight generation, multi-tenant architecture, and production monitoring.

Ready to build?

Ship AI that works
at production scale.

From prototype to production. From platform to population scale.

Talk to our team See case studies