We are building agentic AI systems that do more than answer questions they perceive context, reason across steps, select the right tools, and take action inside real business workflows. These are early initiatives, and they are already working.
Talk to Our ExpertsMost AI deployments today are reactive a user asks a question, the AI responds. Agentic AI is different. It takes a goal, breaks it into steps, chooses which tools or data sources to use, executes autonomously, and returns a result without a human directing each step.
The pattern is: perceive → reason → act. An agentic system takes in unstructured input, applies AI reasoning to understand it, and produces a structured outcome or triggers a downstream action inside systems your business already runs on.
Healthcare providers receive high volumes of scanned documents PDFs, images, faxes that need to be manually matched to the right patient and routed to their record. It is slow, error-prone, and expensive, particularly when scans are imperfect.
We built an agentic pipeline that eliminates the manual step entirely. A document is uploaded. The system classifies it, extracts patient identity fields even from noisy OCR output, matches against a database of 100,000 patient records using fuzzy matching, and returns confidence-scored results ready for automated routing. No human in the loop.
Scanned document (PDF / image)
↓ OCR extraction
↓ Agent: classify document type
↓ Agent: extract patient name + DOB
↓ Fuzzy match → ranked patient records
↓ Confidence-scored results → routing
The same agentic pattern applies wherever unstructured documents need to be matched to structured records insurance claims, legal filings, financial correspondence.
Enterprise MDM platforms generate millions of log events daily device check-ins, policy evaluations, security updates, failures. Extracting insight from them today requires an analyst, a DBA, SQL knowledge, and a waiting queue.
We built a two-layer agentic system. The first layer transforms raw syslog streams into a queryable knowledge base structured fields in PostgreSQL plus semantic vector embeddings that capture meaning beyond schema. The second layer is an agentic query interface: an LLM receives a plain English question, autonomously decides whether to run a SQL query or a semantic similarity search, executes it, and returns a human-readable summary with patterns and anomalies surfaced.
An IT manager with no SQL knowledge can ask: "Were there any security issues on Macs running macOS 14 last month?" and receive a clear, accurate answer in seconds.
Raw syslog stream
↓ Parse + structure → PostgreSQL
↓ Embed → vector store (pgvector)
↓ User asks in plain English
↓ Agent reasons: SQL or semantic?
↓ Execute query autonomously
↓ LLM formats result as plain summary
The same architecture applies to network logs, application logs, security audit trails, and compliance reporting any platform generating high-volume unstructured operational data.
AI assistants are only as useful as the data they can see. Most deployments today give AI a static knowledge base or let it search the web. What they cannot do is reach into the live business systems your teams depend on your ATS, your CRM, your support platform, your internal databases.We built an MCP (Model Context Protocol) server that connects a recruitment platform directly to AI assistants like Claude. Recruiters and hiring managers can ask natural language questions "Show me candidates who applied in the last two weeks" or "Pull up the profile for candidate 4821" and the AI fetches live data and responds, without switching tools, exporting reports, or waiting on engineers.MCP is an emerging standard for connecting AI models to external tools and data sources. We have built against it and can replicate this integration pattern for any API-driven business system.
User asks AI: "Who applied this week?"
↓ AI identifies data need
↓ MCP server receives tool call
↓ Fetches live data from Teamtailor API
↓ Returns structured result to AI
↓ AI responds in natural language
The same pattern works for any business system with an API CRM, ITSM, internal databases, analytics platforms turning your AI assistant from a chatbot into an active participant in your workflows.
Multi-step autonomous workflows that perceive input, reason across stages, and produce structured outcomes without human direction at each step.
Extract, classify, and act on information from unstructured documents PDFs, scanned images, freeform text at scale and with resilience to real-world noise.
Connect AI assistants to your live business systems so they can query, retrieve, and act on real data not just what they were trained on.
Let any team member query complex operational data in plain English logs, databases, records without SQL, dashboards, or analyst bottlenecks.
Combine vector embeddings and SQL so your AI can answer both precise structured questions and conceptual, meaning-driven queries against the same data.
AI agents that autonomously select and call the right tool search, query, API, file system based on intent, without hard-coded routing logic.
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