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AI Engineering · Observability · Dashboards

Accelerate with Agentic AI

0 → 1 AI Engineering — from multi-step stateful agents with reflection loops to multi-agent pipelines, all coordinated by a Workflow Orchestrator. Built for your operations — Finance, Sales, Legal, CX. Pre-built solutions for your industry — Logistics, Energy, Solar, Manufacturing & Design.

4+

Industry Dashboards

4

AI Verticals

8

LLMs Tracked

1

Unified AI Console

What We Build

Three Pillars of AI Engineering

The full stack — from model selection and agent architecture to safety and governance.

Foundation

AI Model Development

  • Context engineering
  • Fine-tuning / post-training
  • Evaluation frameworks
  • Model selection (LLMs, multimodal, specialized models)
  • Performance, latency, cost optimization
Core Product

Agent & Workflow Architecture

  • Multi-step reasoning systems
  • Stateful agents
  • Tool use / function calling
  • Reflection loops
  • Multi-agent systems
  • Workflow orchestration
Trust

Evaluation, Safety & Governance

  • Hallucination mitigation
  • Guardrails
  • Security testing
  • Responsible AI policies
  • Bias, fairness, transparency
  • Regulatory alignment
Industries

Built for Your Operations

AI solutions tailored to the workflow realities of four high-impact operations.

Finance

Risk analytics, portfolio monitoring, and AI-driven forecasting for financial services.

Sales

Pipeline intelligence, lead scoring, and AI chat agents that accelerate revenue.

Digital

Real-time performance dashboards, user analytics, and AI-powered product insights.

CX

Customer experience intelligence — sentiment, CSAT, resolution time, and AI support agents.

Dashboards

Pre-Built. Production-Ready.

Industry-specific dashboards that integrate directly with the AI Observability Console.

Live Demo

See It In Action

Interactive LangGraph agent flow — the same view inside the AI Observability Console. Scroll to zoom, drag to pan, click nodes to inspect.

MCP

MCP Calls Graph

Keep track of all your tool calls

Tool calling by parallel execution using Model Context Protocol (MCP) to fan out across Filesystem, GitHub, Database, and Web Search servers.

Legend

User / Response
Claude AI Model
Tool Planning
MCP Client
Filesystem Server
GitHub Server
PostgreSQL Server
Web Search Server
Aggregated Results
Dashed = active tool call
RAG

RAG Search Strategy — Decision Flowchart

RAG search using only dense index will not give the best results for all prompts - a multi-layered search strategy must be designed based on intent

Follow the decision tree to pick the right retrieval strategy. Click any strategy node to see details.

Legend

Intent Router
Decision point
Hybrid (BM25 + Dense)
Map-Reduce RAG
Hierarchical RAG
Refinement (HyDE / exp.)
Dense vector search
Context + re-rank + LLM
Optional / conditional

Ready to engineer with AI?

Explore the dashboards, dive into LLM benchmarks, or get in touch to build a custom AI solution for your team.