Operations-Heavy Companies
Replace manual triage, classification, and document handling with AI workflows that scale β and stay accurate as your volume grows.
Who we build for
Who We Build AI & Intelligent Automation For
AI lands in production when it removes real work β not when it sits in a demo. We help teams that have repetitive workflows, manual decisions, or untapped data find leverage with AI that is grounded, evaluated, and integrated.
Product Teams Adding AI Features
Embed LLMs, RAG, and recommendation engines into your product the right way β with evaluation, cost control, and PII safety built in.
Businesses Drowning in Manual Processes
Automate the repetitive β invoice processing, data entry, follow-ups β so your team can focus on the work only humans should be doing.
Data-Rich Organisations
Turn dormant docs, tickets, and logs into knowledge bases your team can query β with retrieval pipelines tuned to your domain.
Why Chayaniq for AI
AI & Intelligent Automation Benefits
AI features 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.
Generative AI Integration
LLM-powered features β drafting, summarisation, classification β wired into your product with explicit fallbacks and provider portability.
RAG (Retrieval-Augmented Generation) Pipelines
Ingestion, chunking, embeddings, and retrieval β designed so answers stay grounded as your knowledge base changes and grows.
Intelligent Document Processing
Extract, classify, and route documents at scale β replacing manual data entry with auditable, structured automation.
AI-Powered Recommendation Engines
Recommendations tuned to your catalog and user signals β improving conversion and engagement without bolting on a black-box vendor.
Custom AI Workflow Automation
Long-running, multi-step automations built around your real process β including exceptions humans handle today, not just the happy path.
Prompt Engineering & Fine-Tuning Consultation
Senior guidance on prompts, evaluation suites, and when (and when not) to fine-tune β so you spend AI budget where it actually moves the needle.
Industries we serve
AI & Automation Tuned to Your Vertical
AI delivers leverage when it is grounded in your industry's vocabulary, data, and decision logic. We build AI features and automations 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 360Β° 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 AI & Automation Delivery
AI lands in production when it is 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 β then choose the model and architecture that fit, not the trendiest one.
Data & Retrieval Design
Ingestion, chunking, embeddings, and retrieval β designed so answers stay grounded as your corpus changes and your team adds new sources.
Model Integration & Orchestration
Prompt engineering, tool use, and orchestration with explicit fallbacks β provider-agnostic so you stay portable as the model landscape shifts.
Evaluation & Safety
Eval suites that cover accuracy, safety, latency, and cost β running in CI so regressions are caught before they reach a user.
Deployment
Canary releases, automated rollback, and observability that respects PII boundaries β 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 β your AI gets smarter with every release.
How we deliver AI & intelligent automation
AI lands in production when it removes real work β not when it sits in a demo. We engage at every stage of the AI lifecycle, from feasibility to ongoing optimisation.
LLM-powered features wired into your product the right way β with explicit fallbacks, prompt observability, and cost visibility.
- Drafting, summarisation, classification, and extraction features
- Provider-agnostic orchestration so you stay portable
- Eval suites that catch regressions before users do
Our stack
Our AI & Automation Technology Stack
Provider-agnostic foundations and battle-tested ops β so your AI product stays 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 AI & Intelligent Automation Solutions
Short reads from how we shipβarchitecture, 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 β not bad models. Here is how to avoid all three.
Continue reading
