AIThatDoesUsefulWorkInsideYourOperations
Most practical AI for mid-market businesses is not about large language model experiments. It is about connecting AI to the systems your business already runs so it can classify documents, route requests, surface relevant information, and take action inside your operational workflows.
What Techseria builds with AI
- Document Processing & Classification: Extract structured data from unstructured documents invoices, contracts, purchase orders, compliance forms and route or store them automatically based on content.
- RAG Knowledge Bases: Connect an LLM to your internal documentation, policies, or product data so staff can ask questions and receive accurate, source-cited answers without searching manually.
- Multi-Agent Operational Workflows: Orchestrate sequences of AI steps research, validation, decision, action using LangGraph or Azure AI Foundry. Each agent handles a discrete task; the workflow handles the coordination.
- AI-Assisted Decision Support: Surface the right information at the point a decision is made flagging anomalies in data, summarizing relevant history, or recommending next steps based on operational context.
- LLM Integration with Existing Systems: Add AI capability to ERPNext, Payload CMS, or custom software without rebuilding the underlying platform. API-based integration that fits the tools your team already uses.
When is AI the right investment for your business?
AI adds clear value when these conditions are present:
Technology we use
- Azure OpenAI & Azure AI Foundry: GPT-4o and other models hosted on Azure your data stays within your Azure tenant. Azure AI Foundry provides orchestration, evaluation, and deployment tooling.
- LangChain & LangGraph: For building RAG pipelines and multi-step agentic workflows. LangGraph handles stateful, cyclic agent orchestration where decisions branch based on intermediate results.
- Qdrant: High-performance vector database for semantic search and retrieval-augmented generation. Deployed on Azure for data residency compliance.
- Python: All AI pipeline code is written in Python with full test coverage and handover documentation. No black-box tools that your team cannot maintain.
How we deliver AI projects
- 01Phase 1
Use Case Discovery
We identify the two or three AI use cases with the highest value-to-effort ratio in your specific operational context. Discovery outputs a business case with expected inputs, outputs, and success criteria for each candidate.
- 02Phase 2
Proof of Concept
Before a full build commitment, we deliver a working proof of concept against real data. This validates assumptions about data quality, model performance, and integration complexity eliminating the main risks before production investment.
- 03Phase 3
Production Build & Integration
Fixed-fee build with milestone delivery. The completed system is integrated into your existing operational stack, tested against your data, and documented for handover. Your team can operate and maintain it without ongoing dependency on Techseria.
Impact
Techseria has delivered RAG pipelines, document intelligence systems, and LangGraph-based agent workflows for clients in professional services, manufacturing, and financial services. Projects span Azure OpenAI integration, internal knowledge base assistants, and automated document routing connected to ERPNext.
Frequently asked questions
Have a specific AI use case in mind?
Tell us what you're trying to automate or improve. We'll give you an honest assessment of what's feasible, what it would cost, and how long it would take.