Koca Ventures Ltd
71-75 Shelton Street
Covent Garden, London
WC2H 9JQ, United Kingdom
Registered in England & Wales — 16231043
AGENTIC AI & ON-PREMISE SYSTEMS
Private AI Agents That Run YourOperations, Not Just Your Inbox
Custom agent harnesses, Claude / OpenAI Agent SDK deployments, knowledge graphs, and self-hosted inference — built around the workflow your team already uses. Security-sensitive, on-premise edge AI is our daily work.
WHAT WE BUILD
Six pieces, all production-grade
01
Custom Agent Harnesses
Bespoke agent runtimes built around your workflows — tool calling, memory architectures, retry semantics, and approval gates designed for the class of work your team actually does.
02
Agent SDK Integration
Production deployments on the Claude Agent SDK, OpenAI Agents framework, and MCP — multi-agent orchestration, tool use, structured output, wired into your existing services.
03
Document Agents + Knowledge Graphs
RAG over your contracts, specs, SOPs, and PDFs with citations — backed by an embedded property-graph layer, so the agent reasons across relationships, not just chunk similarity.
04
Data & CRM Pipelines
ETL and ingestion pipelines that feed agents structured context, plus CRM automation — inbound-lead routing, auto-summaries of enquiries, and reliable follow-up workflows.
05
On-Premise AI Deployment
Run agents on your hardware: self-hosted inference, encrypted local storage, signed updates, role-based access, audit logs. No customer data leaves your infrastructure.
06
Edge AI & Computer Vision
NVIDIA Jetson Orin deployments, DeepStream pipelines, TensorRT/ONNX optimisation, and offline-capable operation — edge inference near the cameras for real-time alerts without cloud dependency.
FLAGSHIP USE CASE
WhatsApp AI Operations Organizer
Most companies already run daily operations through WhatsApp: managers ask for updates, field teams send photos and voice notes, sales discuss requests, procurement shares prices. It's fast — but tasks get buried, ownership is unclear, and follow-ups are forgotten.
The Operations Organizer is an AI layer on top of company-approved WhatsApp channels that converts unstructured conversations into tasks, reminders, summaries, decisions, and reports — without forcing teams to learn a new app.
01
AI Message Understanding
Classifies conversations into tasks, issues, approvals, risks, customer requests, procurement needs, and scheduling updates — automatically.
02
Voice Note & Image Processing
Transcribes voice notes, extracts useful information from images and documents, and connects each item to the right project, customer, team, or location.
03
Task & Follow-Up Engine
Creates tasks, assigns owners, tracks due dates, reminds responsible people, and escalates ignored or overdue items.
04
Daily & Weekly Reports
Generates concise summaries for managers: completed work, open issues, delays, risks, spending items, unresolved customer requests, and team performance.
05
Company Knowledge Memory
A searchable internal memory (backed by an embedded property-graph) of past decisions, recurring problems, customer history, supplier notes, and operational patterns.
06
Manager Dashboard
A web-based control panel where leadership can see what is happening across teams without reading hundreds of messages.
Manager queries the system can answer
- “Which jobs are delayed today?”
- “Who is waiting on approval?”
- “Summarise all site updates from this week.”
- “Which customer requests have not been followed up?”
- “Create a task list from yesterday's WhatsApp messages.”
- “Show me procurement items mentioned but not ordered yet.”
SECURITY CREDIBILITY
Edge AI is security-sensitive. We treat it that way.
Our security research has been acknowledged by NVIDIA for the responsible disclosure of a vulnerability in the Jetson edge-AI platform.
That depth carries into every edge-AI deployment: on-premise by default, hardened runtime, signed updates, and device identity treated as defaults, not afterthoughts.
DEPLOYMENT MODEL
Your infrastructure, your data, your control.
- · Camera streams stay local where possible.
- · Documents indexed in private vector + graph stores.
- · Answers include citations to reduce hallucinations.
- · Security controls: network isolation, encrypted storage, audit logs, signed updates, role-based access.
- · External LLM APIs are optional — not the foundation.
FREQUENTLY ASKED
Agentic AI FAQ
What is an agentic AI system?
An agentic AI system is software where an LLM autonomously calls tools, reads data, and executes multi-step workflows toward a goal — not a chatbot that only generates text. Ours read documents, query databases, file tickets, and call APIs under structured human approval.
How is this different from a chatbot?
A chatbot answers questions; an agent acts. Our harnesses include tool calling, retry semantics, memory (often an embedded property-graph), approval workflows, and integration with your real systems — the output is actions performed, not just answers displayed.
Can we run this on our own servers?
Yes — on-premise deployment is a first-class option, not a fallback: self-hosted inference (vLLM, Ollama, llama.cpp), encrypted local stores, signed updates, role-based access. External LLM APIs are optional, not foundational.
Do you use Claude, GPT, or local models?
Whichever fits the workload. Sensitive on-premise work runs local models (Llama, Qwen, Mistral) served via vLLM; where data sensitivity permits and the reasoning is hard, Claude or GPT through their agent SDKs. Many production deployments are hybrid.
Why does a graph layer matter for document agents?
Vector search alone isn't enough. We pair it with an embedded property-graph layer so the agent can traverse “this RFI is about this spec, owned by this engineer, who signed off this revision” in milliseconds — locally, on your own infrastructure, without standing up an external graph service.
What does a typical deployment timeline look like?
A focused pilot around one workflow is typically four weeks: discovery, data intake, demo build, and roadmap review. Full production rollout depends on scope but commonly runs 8–16 weeks after the pilot.
EXPLORE FURTHER
Related work
Last reviewed:
READY TO TALK?
Start with one pain point
Share one workflow that hurts — site updates, customer follow-up, document Q&A, procurement chaos — and we'll build a small demo around your real data before any larger commitment.