Operations-Heavy Companies
Replace manual triage, classification, and document handling with agents that work end-to-end — and stay accurate as your volume grows.
Agentic Workflows
We design and build autonomous AI agents that plan, decide, and execute multi-step business processes — chaining tools, APIs, and human approvals into workflows that run reliably without constant supervision.
Autonomous task planningTool & API orchestrationHuman-in-the-loop approvals
Who we build for
Who We Build Agentic Workflows For
AI lands in production when it removes real work — not when it sits in a demo. We help teams that have repetitive workflows, manual decisions, or untapped data find leverage with autonomous agents that are grounded, evaluated, and integrated.
Product Teams Adding AI Features
Embed agentic features into your product the right way — with evaluation, cost control, and PII safety built in from the start.
Businesses Drowning in Manual Processes
Automate the repetitive — invoice processing, data entry, follow-ups — so your team can focus on the work only humans should be doing.
Data-Rich Organisations
Turn dormant docs, tickets, and logs into agent-ready knowledge bases — with retrieval pipelines tuned to your domain.
Why Chayaniq for agentic AI
Agentic Workflow Benefits
AI agents go beyond demos when they are grounded in your data, evaluated against your goals, and integrated with the tools your team uses. Here is what we bring to the table.
Autonomous Agent Development
Agents that plan, decide, and execute multi-step tasks — wired into your product with explicit fallbacks and provider portability.
RAG-Grounded Decision Making
Ingestion, chunking, embeddings, and retrieval — designed so agent decisions stay grounded as your knowledge base changes and grows.
Tool & API Orchestration
Agents that call your APIs, internal tools, and third-party services — chaining actions together to complete real tasks.
Human-in-the-Loop Approval Gates
Approval steps where stakes are high — so agents move fast on routine work and pause for judgement calls.
Custom Workflow Automation
Long-running, multi-step automations built around your real process — including exceptions humans handle today, not just the happy path.
Strategy & Architecture Consultation
Senior guidance on agent architecture, evaluation, and when (and when not) to use agents — so you spend AI budget where it actually moves the needle.
Industries we serve
Agentic Workflows Tuned to Your Vertical
AI agents deliver leverage when they are grounded in your industry's vocabulary, data, and decision logic. We build agentic workflows across SaaS, e-commerce, healthcare, finance, manufacturing, logistics, real-estate, construction, and insurance.
Remote patient monitoring
- Real-time collection of wearables data
- Enhanced doctor-patient communication
- Comprehensive view of patient health data
- Data-driven report generation
- Seamless integration with existing systems
- Improved patient outcomes
Electronic medical records (EMR)
- Interoperability of medical data across departments
- HIPAA / GDPR compliant solutions
- Improved patient care
- Quick access to patient records, vitals, and lab results
- Enhanced security and privacy of patient health data
Document management
- Compliant documentation workflow
- Streamlined management of health information
- Integrations with existing systems for a 360° data view
- Secure file sharing and access management
Insurance verification
- Automated medical insurance eligibility
- Proactive identification of coverage issues
- API-ready and HIPAA-compliant
How we work
Our Process for Agentic Workflow Delivery
AI agents land in production when they are grounded in your data, evaluated against your goals, and integrated with your tools. Our process bakes that discipline into every iteration.
Discovery & Use-Case Framing
We start with the use case, the data you have, and the failure modes you cannot tolerate — then choose the model and architecture that fit, not the trendiest one.
Data & Retrieval Design
Ingestion, chunking, embeddings, and retrieval — designed so answers stay grounded as your corpus changes and your team adds new sources.
Agent & Orchestration Build
Prompt engineering, tool use, and orchestration with explicit fallbacks — provider-agnostic so you stay portable as the model landscape shifts.
Evaluation & Safety
Eval suites that cover accuracy, safety, latency, and cost — running in CI so regressions are caught before they reach a user.
Deployment
Canary releases, automated rollback, and observability that respects PII boundaries — so AI features ship with the same discipline as the rest of your stack.
Monitoring & Continuous Improvement
Drift detection, abuse signals, and analytics that feed back into the next iteration — your agents get smarter with every release.
How we deliver agentic workflows
AI agents earn their place when they remove real work — not when they sit in a demo. We engage at every stage, from feasibility to ongoing optimisation.
Agents that plan, decide, and execute multi-step tasks — wired into your product with explicit fallbacks, prompt observability, and cost visibility.
- Planning, tool-use, and multi-step task execution
- Provider-agnostic orchestration so you stay portable
- Eval suites that catch regressions before users do
Our stack
Our Agentic Workflow Technology Stack
Provider-agnostic foundations and battle-tested ops — so your agents stay portable, evaluated, and observable as the model landscape shifts.
Models & Providers
- Anthropic Claude
- OpenAI GPT
- Google Gemini
- Mistral
- Llama (open / fine-tuned)
AI Frameworks
- LangChain
- LlamaIndex
- Vercel AI SDK
- DSPy
- Custom orchestrators
RAG & Vector Stores
- pgvector
- Pinecone
- Weaviate
- Qdrant
- Redis vector
Automation & Orchestration
- n8n
- Zapier
- Temporal
- Airflow
- Custom Node services
Evaluation
- LangSmith
- Promptfoo
- Custom eval suites
- Tracing
Compute & Serving
- AWS SageMaker
- Lambda
- Modal
- Replicate
- vLLM / Ollama
Perspectives
Ideas that pair with Agentic Workflows
Short reads from how we ship—architecture, product, and ops. Same themes as this service, different angles.
March 2026
RAG Pipelines Explained: Giving Your AI App a Long-Term Memory
Vector databases, chunking strategies, embedding models, and a step-by-step RAG architecture that actually works in production.
Continue readingJanuary 2026
What Most Teams Get Wrong About Adding AI to Their Product
AI features fail in production because of bad scoping, weak evaluation, and missing fallbacks — not bad models. Here is how to avoid all three.
Continue reading
