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NeuralChetna

The Intelligence Cortex

AI is one region of the brain — not the entire brain.

A reliable organisation cannot rely only on large language models. NeuralChetna designs the right combination of models, rules, algorithms, workflows, knowledge and human judgement — and is explicit about which part carries which responsibility.

The composition

What an AI Brain is actually made of

Probabilistic components sit inside a deterministic, governed system — with human judgement above all of it, not beside it.

“NeuralChetna does not place an LLM at the centre of every decision. We design the right combination of models, rules, algorithms, workflows, knowledge and human judgement.”

  1. Human judgement

    • Approval
    • Escalation
    • Accountability
    • Context

  2. Governance and control

    • Policy controls
    • Guardrails
    • Evaluation
    • Audit
    • Observability
    • AI security

  3. Deterministic orchestration

    • Workflow engines
    • Business rules
    • Algorithms
    • APIs
    • Tool integration
    • MCP integrations

  4. Models

    • Large language models
    • Small language models
    • Machine learning
    • Vision models
    • Speech models

  5. Knowledge and retrieval

    • Retrieval systems
    • Knowledge graphs
    • Search
    • Document stores
    • Prompt and model registries

Capabilities

What we do in this region

From the first question about where AI is worth using, through to the operational discipline of running it.

Direction

  • AI strategy
  • AI opportunity assessment
  • Model selection
  • Build-versus-buy decisions
  • AI operating model

Building

  • LLM integration
  • Prompt and instruction architecture
  • Retrieval-augmented generation
  • Model adaptation and fine-tuning
  • Small language models
  • Multimodal AI
  • Agentic workflows
  • Tool integration
  • Model Context Protocol integrations
  • Deterministic orchestration

Keeping it trustworthy

  • Human-in-the-loop design
  • AI governance
  • Evaluation
  • Guardrails
  • Observability
  • AI security
  • Model lifecycle management

Running it

  • AI infrastructure
  • Production deployment
  • Cost and latency management
  • Workforce enablement
  • Adoption and change

Principles

How we design intelligent systems

These are the rules we apply, and the ones we will argue for in a workshop.

  1. The model is a component, not the architecture

    A language model is one part with a bounded responsibility. The system around it decides what it is allowed to touch.

  2. Deterministic where determinism is possible

    If a rule can be written, write the rule. Reserve probabilistic components for problems that genuinely need them.

  3. Retrieval is a curation problem

    Retrieval quality is set by what the organisation has chosen to keep, curate and retire — not by the embedding model.

  4. Evaluate before you deploy, and after

    Agree the evaluation criteria before building. Keep measuring once it is live, including human override rates.

  5. Design the human control points first

    Decide what requires approval, what can be reversed, and what must never be automated — then build around that.

  6. Assume you will be asked to explain it

    Regulators, auditors, customers and your own board will ask. Build the trace at the start; it cannot be retrofitted.

Questions we are asked

Common questions

Does NeuralChetna build LLM applications?
Yes, where an LLM is the right component. We also build systems where the language model is a small part of the design, and systems where no language model is involved at all. The decision follows the problem, not the technology.
What does "deterministic orchestration" mean in practice?
It means the workflow, the permissions, the data access and the escalation rules are implemented as ordinary, testable code. The model contributes bounded steps — extraction, drafting, ranking, summarising — inside a system whose behaviour can be predicted and audited.
How do you know an AI system is still working?
Evaluation sets agreed before the build, monitored continuously after it, plus observability on inputs, outputs, latency, cost and human overrides. If a system cannot be evaluated, we say so before it is built.

Next step

Bring us the decision, not the technology.

The most productive first conversation is about a decision that is currently slow, inconsistent or unexplainable — not about which model to use.

Start a Neural Conversation