Foundation
Bring together raw structured and unstructured operational data with the context needed to interpret it.
Building intelligent data products
AI as the transformation layer between enterprise data and business action
DataKnobs converts structured and unstructured data into predictive intelligence, operational insight, LLM-powered experiences and autonomous agentic systems: using the AI technique that fits the problem.
Predictive ML
LLMs & reasoning
Agentic automation
1 · ContextEnterprise data is the foundation
Structured tables, telemetry, documents, interactions and operational history.
2 · IntelligenceApply the right AI paradigm
Statistical methods, predictive ML, LLMs or agents: selected by the task and constraint.
3 · OutcomeActivate decisions and experiences
Predictions, automation, personalization, recommendations and intelligent products.
A different approach
Do not force every business problem through the same AI model
DataKnobs uses a multi-paradigm AI stack. The goal is not to maximize model novelty; it is to choose the simplest, strongest technique that converts available data into a measurable business outcome.
Transformation
Engineer signals and apply predictive, statistical, generative or agentic intelligence according to the use case.
Activation
Deliver decision-ready intelligence through applications, workflows, APIs, agents and user-facing data products.
Spectrum of AI capabilities
Use the right intelligence for the job
Predictive ML, language models, statistical techniques and autonomous agents solve different classes of problems. DataKnobs brings them together around the data and the desired outcome.
Predictive intelligence
Custom machine-learning models extract forward-looking signals from operational data so teams can act before an outcome occurs.
Real-world caseData-center switchgear health
Voltage and telemetry data are converted into equipment health scores to support predictive maintenance and risk reduction.
Large language models
LLMs transform unstructured content into structured insight, summaries, explanations, recommendations and interactive experiences.
Real-world caseStock earnings-call analysis
Transcripts are analyzed for sentiment, risks and opportunities, then converted into structured summaries for faster investment research.
Operational intelligence
Classical methods remain ideal for frequent, explainable decisions where computational efficiency and statistical rigor matter.
Typical capabilitiesExperimentation, anomaly detection & trends
A/B testing, statistical comparisons, anomaly detection and trend analysis provide measurable operating feedback.
Autonomous AI systems
Agents orchestrate tools, data, models and workflows to complete multi-step business tasks with bounded autonomy.
Major advancementFrom answers to actions
Agentic systems can monitor conditions, analyze context, prepare decisions and trigger workflows instead of only generating text.
Design principle
The model is one component. The system is the product.
Production value comes from connecting enterprise context, intelligence, controls and user workflows: not from choosing a model in isolation.
Context first
Give intelligence the business data, definitions and history required to make the output meaningful.
Measure continuously
Track quality, cost, latency and business outcomes so model and workflow choices remain evidence-based.
Activate safely
Move from insight to automation with explicit controls, review points and operating boundaries appropriate to the use case.
AI agents for business operations
Integrated systems that analyze, decide and prepare action
These examples combine data pipelines, analytics, LLM reasoning and workflow automation around a specific operational outcome.
E-commerce Analytics Agent
Analyzes user engagement, conversion funnels and behavioral patterns at scale.
Impact: reduces manual reporting and keeps decision-ready intelligence available continuously.
Compliance Analysis Agent
Reviews chats, emails and calls for quality issues, complaint signals and compliance concerns.
Impact: surfaces recurring patterns and reduces the amount of interaction review that must be performed manually.
Regulatory Monitoring Agent
Analyzes external interactions for signals that may indicate potential regulatory violations.
Impact: assigns risk signals and supports earlier review before problems compound.
AI Webmaster Agent
Combines predictive models, data pipelines and LLM reasoning to manage content, site health and continuous optimization.
Impact: connects monitoring, analysis and maintenance into one operating loop.
Platforms we've built
Productize the capability so teams can reuse it
DataKnobs turns repeatable AI patterns into platforms that help teams create intelligent websites, bots and measurable experiments.
Kreate Websites
An AI-driven website platform for generating, structuring and operating web experiences with content, data and automation.
Visit kreatewebsites.com →Kreate Bots
A platform for building LLM-powered workflows, conversational systems and intelligent automated assistants.
Visit kreatebots.com →ABExperiment
Experimentation and evaluation capabilities for testing website experiences, AI workflows, prompts and measurable outcomes.
Visit abexperiment.com →From data to operating intelligence
Build the AI system around the outcome: not around the buzzword
Start with the data, the decision and the operating constraint. Then select the combination of models, context, evaluation and automation that earns its place in production.