Dataknobs – Trusted AI Platform for Financial Data Products



1. Cover Page

  • Title: Dataknobs – Build, Govern, and Optimize AI Data Products
  • Tagline: “Turn data into intelligent products — safely, fast, and at scale.”
  • Background visual: abstract data network / circuit motif

2. Mission & Vision

Vision: To be the leading platform that empowers data teams to effortlessly create valuable and explainable data products.

Mission: To democratize data product development by providing an intuitive and scalable platform that accelerates innovation, ensures integrity, and fosters collaboration between humans and AI.


3. Dataknobs at a Glance

A unified suite for data product creation, governance, and optimization. It connects GenAI, agentic workflows, and cloud-native data engineering to deliver end-to-end intelligence.

Core Components:

Layer Product Core Focus
Creation KREATE Rapidly build AI assistants, data products, and websites
Governance KONTROLS Enforce compliance, lineage, and explainability
Optimization KNOBS Experiment, diagnose, and improve AI outcomes

4. KREATE – Rapid Creation Engine

Kreate is the creation factory of Dataknobs. It transforms data into signals, insights, and interactive experiences.

Core Capabilities:

  • KreateDataProducts: Build structured data signals and APIs.
  • KreateWebsites: Generate AI-optimized, self-evolving websites.
  • KreateBots: Design domain-specific AI assistants with RAG and personalization.
  • KreateCMS: Manage hybrid AI + human content with lineage tracking.

Key Benefits:

  • Rapidly create data-driven web and AI products
  • Cloud-native scalability and automation
  • Seamless integration with Kontrols (governance) and Knobs (experimentation)

5. KONTROLS – Governance and Trust Layer

“Keep your AI systems compliant, predictable, and under control.”

Sub-Products:

  • GateKeep: Filters unsafe or irrelevant inputs.
  • Enforcer: Applies runtime policies and controls during execution.
  • Shield: Moderates and sanitizes outputs for safety and brand trust.

Core Strengths:

  • Multi-layered governance (input → runtime → output)
  • Transparent auditing and lineage
  • Integration hooks across LLMs, APIs, and agents

6. KNOBS – Experimentation and Optimization

“Understand before you optimize.”

Knobs empowers teams to experiment, observe, and improve model behavior safely.

Sub-Products:

  • ABExperiment: Run structured A/B or multivariate tests for models and prompts.
  • KnobScope: Trace and diagnose model or agent performance in real-time.
  • ResultBench: Benchmark and evaluate performance quantitatively and qualitatively.

Value Proposition:

  • Continuous feedback loops for AI performance
  • Statistical rigor with human + LLM evaluation
  • Integrated telemetry across experiments

7. Unified Value – The Data Product Factory

Dataknobs bridges the R&D vs. validation gap. It allows teams to move from raw data → insight → trusted AI product with built-in governance.

Why Customers Choose Dataknobs:

  • Speed + Safety: Fast prototyping with full governance
  • Unified Platform: One framework from creation to evaluation
  • Explainability: Audit and lineage from prompt to output
  • Scale: Cloud-native, modular, and enterprise-ready

8. Key Use Cases

  • Complaint Management with AI
  • Audit Sales & Collection Calls
  • E-commerce Analysis
  • Financial Planner AI Assistant
  • Tax Research AI Assistant
  • Stocks Earnings Call Momentum Scoring
  • AI Twin for Data Centers




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Dataknobs Blog

Showcase: 10 Production Use Cases

10 Use Cases Built By Dataknobs

Dataknobs delivers real, shipped outcomes across finance, healthcare, real estate, e‑commerce, and more—powered by GenAI, Agentic workflows, and classic ML. Explore detailed walk‑throughs of projects like Earnings Call Insights, E‑commerce Analytics with GenAI, Financial Planner AI, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, and Real Estate Agent tools.

Data Product Approach

Why Build Data Products

Companies should build data products because they transform raw data into actionable, reusable assets that directly drive business outcomes. Instead of treating data as a byproduct of operations, a data product approach emphasizes usability, governance, and value creation. Ultimately, they turn data from a cost center into a growth engine, unlocking compounding value across every function of the enterprise.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

Our structured‑data analysis agent connects to CSVs, SQL, and APIs; auto‑detects schemas; and standardizes formats. It finds trends, anomalies, correlations, and revenue opportunities using statistics, heuristics, and LLM reasoning. The output is crisp: prioritized insights and an action‑ready To‑Do list for operators and analysts.

AI Agent Tutorial

Agent AI Tutorial

Dive into slides and a hands‑on guide to agentic systems—perception, planning, memory, and action. Learn how agents coordinate tools, adapt via feedback, and make decisions in dynamic environments for automation, assistants, and robotics.

Build Data Products

How Dataknobs help in building data products

GenAI and Agentic AI accelerate data‑product development: generate synthetic data, enrich datasets, summarize and reason over large corpora, and automate reporting. Use them to detect anomalies, surface drivers, and power predictive models—while keeping humans in the loop for control and safety.

KreateHub

Create New knowledge with Prompt library

KreateHub turns prompts into reusable knowledge assets—experiment, track variants, and compose chains that transform raw data into decisions. It’s your workspace for rapid iteration, governance, and measurable impact.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

A pragmatic playbook for CIOs/CTOs: scope the stack, forecast usage, model costs, and sequence investments across infra, safety, and business use cases. Apply the framework to IT first, then scale to enterprise functions.

RAG for Unstructured & Structured Data

RAG Use Cases and Implementation

Explore practical RAG patterns: unstructured corpora, tabular/SQL retrieval, and guardrails for accuracy and compliance. Implementation notes included.

Why knobs matter

Knobs are levers using which you manage output

The Drivetrain approach frames product building in four steps; “knobs” are the controllable inputs that move outcomes. Design clear metrics, expose the right levers, and iterate—control leads to compounding impact.

Our Products

KreateBots

  • Ready-to-use front-end—configure in minutes
  • Admin dashboard for full chatbot control
  • Integrated prompt management system
  • Personalization and memory modules
  • Conversation tracking and analytics
  • Continuous feedback learning loop
  • Deploy across GCP, Azure, or AWS
  • Add Retrieval-Augmented Generation (RAG) in seconds
  • Auto-generate FAQs for user queries
  • KreateWebsites

  • Build SEO-optimized sites powered by LLMs
  • Host on Azure, GCP, or AWS
  • Intelligent AI website designer
  • Agent-assisted website generation
  • End-to-end content automation
  • Content management for AI-driven websites
  • Available as SaaS or managed solution
  • Listed on Azure Marketplace
  • Kreate CMS

  • Purpose-built CMS for AI content pipelines
  • Track provenance for AI vs human edits
  • Monitor lineage and version history
  • Identify all pages using specific content
  • Remove or update AI-generated assets safely
  • Generate Slides

  • Instant slide decks from natural language prompts
  • Convert slides into interactive webpages
  • Optimize presentation pages for SEO
  • Content Compass

  • Auto-generate articles and blogs
  • Create and embed matching visuals
  • Link related topics for SEO ranking
  • AI-driven topic and content recommendations