AI / LLM
Model choice, retrieval, and orchestration selected for the workflow.
- OpenAI
- Azure OpenAI
- Claude
- Gemini
- Qwen
- LangChain
- LangGraph
- RAG
- Agent orchestration
- Vector search
- MCP
AI product engineering
AI systems that solve real business problems.
Oppuna Labs designs and builds production-grade AI agents, intelligent automation, enterprise knowledge systems and custom AI products.
We work from problem definition through architecture, implementation, integration and production deployment.
- Agents
- RAG
- Automation
- Voice AI
- Document Intelligence
- Custom AI Products
Solutions
AI should solve a business problem — not create another experiment.
Oppuna Labs takes an operational or product problem and designs, builds and deploys the AI system that addresses it.
Customer Operations
Build AI agents that answer questions, perform actions, access business systems and escalate to humans when necessary.
Examples
- Support automation
- Customer self-service
- Voice assistants
- CRM integrations
Internal Knowledge
Turn company documents, policies, databases and systems into citation-grounded AI knowledge tools.
Examples
- Enterprise search
- Internal copilots
- Policy assistants
- Regulatory intelligence
- Knowledge retrieval
Document Intelligence
Transform unstructured documents into validated structured workflows.
Examples
- Invoice extraction
- Contract analysis
- Claims processing
- Forms
- PDFs
- OCR pipelines
- Human approval workflows
Workflow Automation
Connect AI reasoning with real business systems.
Examples
- Email → analysis → approval → action
- CRM automation
- ERP integrations
- Case management
- Operational workflows
AI Product Development
Take an AI product from concept to production.
Examples
- SaaS products
- AI-native applications
- Internal platforms
- Mobile applications
- Enterprise tools
Voice AI
Build real-time conversational systems.
Examples
- Customer support
- Appointment handling
- Lead qualification
- Internal assistants
- Multilingual workflows
AI agents
Agents that can actually do work.
An Oppuna Labs agent is a system: reasoning, knowledge, tools, business software, human approval, and a record of what happened.
System flow
User / Business Event
A person, document, message, call, or system event starts the work.
AI Agent
A bounded agent with a defined job and only the context it is allowed to see.
Reasoning / Orchestration
The agent plans the next step, including multi-step and multi-agent workflows.
Enterprise Knowledge / RAG
Retrieval stays inside approved documents, policies, and records, with sources attached.
Tools & APIs
The agent calls specific functions to read or update business systems.
Business Systems
CRM, ERP, email, case tools, and internal APIs where the work already lives.
Human Approval where required
Actions that need a person pause until someone approves them.
Audit / Evaluation / Observability
Decisions, tool calls, failures, and cost stay reviewable after the fact.
Capabilities
Tool calling
Agents invoke approved functions as part of the workflow.
Multi-agent workflows
Separate roles when a handoff should stay explicit.
MCP integrations
Connect agents to tools through the Model Context Protocol.
Retrieval
Ground answers in the documents and records you choose.
Memory
Keep only the context a workflow is allowed to reuse.
API integrations
Read and write the systems the business already runs.
Guardrails
Constrain inputs, outputs, and actions before they proceed.
Human-in-the-loop
Pause for approval when the action needs a person.
Evaluation
Measure quality against cases from the real workflow.
Auditability
Record what the system saw, decided, and did.
Observability
See failures, latency, and cost after go-live.
How we work
From business problem to production system.
01
Understand
We start with the workflow, users, systems and measurable business problem.
02
Design
Architecture, model selection, data strategy, security, integrations and evaluation criteria.
03
Build
Rapid implementation of the working system using production-quality engineering.
04
Validate
Real-world evaluation, human review, failure testing, guardrails and performance measurement.
05
Deploy & Improve
Production deployment, monitoring, iteration and handover.
Typical engagement models
Fixed-scope project
A defined problem, outcome, and delivery boundary.
AI proof of value
A narrow workflow used to learn whether AI should go further.
Production implementation
The system, integrations, evaluation, and deployment.
Engineering retainer
Continued iteration after the first production release.
Architecture + delivery engagement
Design and implementation carried by the same team.
Engineering
AI is only useful when the engineering around it works.
The model is one component. A production system also needs applications, APIs, data, infrastructure, and a way to see what it did. Platforms are chosen per engagement. A name on this list is an implementation option, not a partnership or certification.
Backend
Services and APIs that agents and operators can call reliably.
- Node.js
- TypeScript
- Python
- Java
- Go
- REST APIs
- Event-driven systems
- Microservices
Cloud
Run the system where the data and the operators already are.
- AWS
- Azure
- GCP
- Docker
- Kubernetes
- Terraform
- Serverless
Data
Store records, cache state, and retrieve the right context.
- PostgreSQL
- MongoDB
- Redis
- Vector databases
- Data pipelines
Applications
Interfaces for the people who use and supervise the system.
- React
- Next.js
- Angular
- React Native
- Flutter
Production
Built for production, not just demos.
Production AI systems need more than a prompt.
Oppuna Labs designs systems with:
- Role-based access
- Human approval
- Audit logs
- PII handling
- Data boundaries
- Prompt-injection defenses
- Guardrails
- Evaluation pipelines
- Model fallback strategies
- Observability
- Cost controls
Products
We build our own AI products too.
Oppuna is an example of the team designing and shipping an end-to-end AI product.
Oppuna
Private AI-powered mental wellness and journaling.
Oppuna combines private AI-powered journaling, guided reflection and wellness workflows in a consumer application designed with privacy and responsible AI considerations.
About
Senior engineering without enterprise bureaucracy.
Oppuna Labs is a product engineering studio. The same practice that ships Oppuna, a private AI wellness application, applies that production discipline to client systems.
Hands-on Engineering
Architecture and implementation are not separated into layers of account management.
AI + Software Engineering
We build the complete system around the model — APIs, applications, integrations, security and infrastructure.
Vendor-neutral AI
We select models and architecture based on the problem rather than forcing every use case onto one LLM.
Production-first
Evaluation, reliability, security and observability are treated as engineering requirements from day one.
Contact
Have a business problem that AI could solve?
Tell us about the workflow, bottleneck or product you want to build.
We’ll help determine whether AI is actually the right solution — and if it is, how to take it into production.