ENTRY 01 · KALAM VENTURES

Agents that operate.Not agents that demo.

Kalam AI designs and deploys production AI agents on Databricks and Hermes — plus AI, Machine Learning, Computer Vision, Deep Learning and M.Tech CS project delivery, built industry-graded.

DATABRICKSHERMESLANGGRAPHM.TECH CS
kalam-ai — status
$ kalam init --house=kalam-ventures
→ connecting to Databricks Mosaic AI... done
→ connecting to Hermes multi-agent core... done
→ loading LangGraph orchestration graph... done
→ agents: production-ready, monitoring: active
→ scope: AI · ML · Computer Vision · Deep Learning
$ status: online
SYSTEM STATUS

Six capabilities, all online

Every card below is a live part of how Kalam AI builds. Scroll down for the architecture behind each.

01
ArchitectureDatabricks Agents

We build agents directly inside Databricks — using Mosaic AI to orchestrate retrieval, reasoning and tool-calling against your own data, governed by Unity Catalog.

Your Data
Mosaic AI
Agent Logic
Live Endpoint
PlatformDatabricks Mosaic AI
GovernanceUnity Catalog-aware access
GroundingVector search & RAG on your data
DeploymentServed model endpoint, not a notebook
02
ArchitectureHermes Multi-Agent

Hermes is our framework for deploying large hierarchical agent systems — specialised agents grouped by function, coordinated centrally rather than running as isolated bots.

Hermes Core
Task Agents
Support Agents
Ops Agents
Monitor Agent
StructureHierarchical, function-grouped agents
ChannelsTelegram & enterprise integrations
ResilienceAuto-recovery after crash or restart
CostTracked per agent, not estimated
ARCHITECTURE 03

AI, ML, Computer Vision & Deep Learning

Engineering and M.Tech CS project delivery — graded like an industry deliverable, not a classroom shortcut.

INDUSTRY
GRADED

Every brief — AI, ML, Computer Vision or Deep Learning — is treated as a real deliverable: the same model selection, evaluation rigor and deployment standard we'd use for a paying client, not a scaled-down academic shortcut.

AI & ML

Supervised, unsupervised and reinforcement learning builds.

Computer Vision

Detection, classification and image-analysis pipelines.

Deep Learning

Neural architectures trained and evaluated properly.

Documentation

Written to hold up under a real viva or defense.

DEPENDENCIES

The stack behind every agent

Platform-native tools, chosen for reliability over novelty.

databricks / mosaic-aiLINKED
hermes / multi-agent-coreLINKED
langgraphLINKED
langchainLINKED
python 3.xLINKED
vector-dbLINKED
unity-catalogLINKED
telegram-bot-apiLINKED
dockerLINKED
postgresqlLINKED
BUILD SEQUENCE

Five steps, every build

The same sequence whether it's a Databricks agent, a Hermes deployment, or an M.Tech project.

01

Discover

Understand the job the agent needs to do and the data it touches.

02

Design

Choose the platform, architecture and model fit for the brief.

03

Build

Orchestrate, train and evaluate against real scenarios and benchmarks.

04

Deploy

Ship to production — served endpoint or live agent deployment.

05

Monitor

Track uptime, cost and behaviour once it's live.

02PLATFORMS — DATABRICKS & HERMES
24/7AGENT UPTIME & MONITORING
100%END-TO-END, SCOPE TO DEPLOYMENT
05STEP BUILD SEQUENCE
philosophy.log
kalam-ai — philosophy.log
"An agent is judged by what it does when no one's watching."
# A demo is impressive for five minutes.
# What matters is whether it's still running,
# still accurate, still affordable — a month later,
# unattended. That's the standard we build to.
$ kalam init --project="your brief"

Build your agent.

Databricks, Hermes, or an AI/ML/CV/Deep Learning M.Tech project — tell us the brief on WhatsApp and we'll scope it.