Capabilities AI & Agentic Systems
AI that does useful work inside your operations \u2014 not just answers questions.",
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 \u2014 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 \u2014 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 \u2014 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:
- Your team spends significant time manually reading, classifying, or extracting data from documents
- Staff repeatedly answer the same questions from internal knowledge that exists but is hard to find
- A process involves pulling together information from multiple systems before a decision can be made
- You want to automate a workflow that requires understanding of text or context, not just structured rules
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
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.
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 \u2014 eliminating the main risks before production investment.
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.
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
Do we need to share our data with OpenAI?
No. We use Azure OpenAI Service, which means models run within your Azure tenant. Your data is not used to train OpenAI models and does not leave your environment.
Can AI integrate with our existing ERP or CRM?
Yes. We connect AI systems to ERPNext, Salesforce, Microsoft Dynamics, and custom platforms via API. The AI system reads from and writes back to your existing operational tools.
What data quality do we need before starting an AI project?
This varies by use case. We assess data quality during the discovery phase and tell you clearly what, if anything, needs to be addressed before a production build is viable.
Do you offer a proof of concept before full commitment?
Yes. Every AI engagement begins with a time-boxed proof of concept scoped to your highest-priority use case. This validates technical feasibility and business value before a production build commitment.
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.