Most AI answers and stops. Ours runs a real loop — sense, decide, act — across every pillar. A five-tier model routing chain (local ONNX → Phi-4 → Qwen → Sarvam → closed API), the agents and prompts that drive it, and the governance that keeps every action honest and auditable.
Twelve applied AI modules, a library of workflow nodes and governance on every action.
The essentials your teams reach for every day — each backed by real objects in the platform.
A five-tier chain from local ONNX to closed API deploys the right model for every job.
Agents, tools, pipelines and MCP work over a RAG knowledge base of your own data.
Guardrails, evaluations, drift monitoring, budgets and full inference logs keep AI honest.
Real events arrive; the Flow-Cognition Agent senses, decides and acts on its own — in seconds. Pick a scenario.
Twelve product-facing AI modules — each grounded in your own data, governed by default and driven by the Flow-Cognition Agent. Explore any of them.
Conversational AI on web, WhatsApp & app — multi-language, intent-aware.
Transcribe, analyse and act on every call — in English and Indian languages.
Read emotion across calls, chats and tickets — with trend alerts.
Right work to the right person — skill-based, load-balanced, prioritised.
Forecast revenue, predict churn and score leads before they slip.
Score and group leads & customers automatically, in real time.
Draft replies, summaries and content grounded in your records.
Catch outliers across finance, ops & security and flag compliance risk.
Surface the next best action, product or offer — every time.
Uncover success patterns, failure sequences and hidden correlations.
Track habits, map journeys and predict intent before customers act.
Enforce policy and support DPDP-aligned compliance across your own frameworks.
The Flow-Cognition Agent takes the routine work off your team — grounded in your own records and bounded by the rules you set.
Follow-ups, summaries, reminders and data entry are drafted or completed for your team instead of sitting in a queue.
New leads, tickets and overdue invoices are sensed and actioned as they arrive — not when someone next opens the queue.
Every action follows your policy, stays within field-level permissions and is logged — so quality holds as volume grows.
20 objects power AI. Filter by department or search to find exactly what you need.
Every model the platform can call — local or hosted — with its tier, cost and residency.
Decide which tier answers a request — cheapest local model first, escalating only when it cannot cope.
A configured assistant with a purpose, a toolset and a scope of data it may touch.
Versioned prompts, so a change to how the model is asked is a reviewable event, not a silent edit.
The documents an agent may retrieve from, and the residency rules that govern them.
The vector store behind retrieval — its dimensions, size and freshness.
Every model call, with tier, tokens, latency and cost — so AI spend is observable, not a mystery line item.
A Model Context Protocol server the platform connects to — the process that actually exposes tools to an agent.
Every tool an agent may call, with the scope it needs and the risk it carries. Tools are records, not free text — so Security can govern them.
A named chain of AI steps — the pipeline the builder produces. Nodes across categories: input, retrieve, model, tool, branch, guardrail, output.
One complete execution of an agent — the container that holds every model call, tool call and retrieval in a single reasoning chain.
One agent delegating to another — the agent-to-agent protocol record, with the task passed, the context shared, and the result returned.
Where a registered model actually runs — the ONNX runtime, GPU pool or hosted endpoint that serves inference.
The rules an AI answer must satisfy before it reaches a customer or writes to a record.
Scored test runs against a golden set, so a model or prompt change is proven better, not merely newer.
Datasets used for offline training, with their lineage, consent basis and residency.
A hard cap on AI spend or call volume, per agent, team or period — so inference cost cannot run away unnoticed.
Watches a model in production for quality decay — an evaluation is a moment; drift is the trend.
What the model actually retrieved before it answered — the chunks, their scores, and whether the answer was grounded in them.
A person’s verdict on an AI output — accepted, corrected or rejected. The record that closes the loop on an agent set to Suggest.
Every action the agent takes is bounded, permissioned and logged — and your data stays in India.
Built for India's Digital Personal Data Protection Act — consent, purpose and residency handled.
Your records and AI run on infrastructure with data residency in India.
The agent only ever sees and touches the fields each role is allowed to.
Policies and evaluations keep outputs on-brand, safe and measured.
Per-team AI budgets and quotas — no runaway model bills.
Every inference and action is logged — who, what, which model and why.
Build table and pivot reports on any object, group and aggregate, then schedule them to land in inboxes automatically.
Flat rows-and-columns grid — one row per record. Best for record-level auditing, raw data review, and exports.
Cross-tabulated matrix with Fields, Columns, and Rows axes. Best for management summaries and cross-dimensional comparison.
You choose, per agent. Agents can run in suggest mode, where a person approves each output, or act automatically within the guardrails and permissions you set.
Only your own records in the platform, retrieved through a governed RAG knowledge base — and only the fields each role is allowed to see.
On infrastructure with data residency in India, built for the DPDP Act 2023. Every inference and action is logged with who, what, which model and why.
Guardrail policies check every output, evaluations score changes against a golden set, and drift monitoring watches for quality decay. A five-tier model routing chain picks the right model for each job.
See which AI capabilities are live for your plan, and what is coming next. Start free, or let us map it to how your team works.
The node categories and agents that power AI Studio. Explore them all in the node catalog.
