PlaniN Lab

Not Just Agents. Not Just Wrappers.

PlaniN works at the foundation — where the real AI breakthroughs happen

Most AI work today is about building agents, chatbots and workflow wrappers on top of existing foundation models. PlaniN does that too — but our primary focus is deeper. We work at the foundational and frontier level — researching and building the fundamental tools, techniques and architectures that make AI systems genuinely more capable, reliable and useful for specific high-stakes domains.

Our particular focus is the layer between the user interface and the language model — the orchestration, context, retrieval, verification and optimisation layer that determines whether an AI system actually performs well in a real professional context. This is where we believe the most important and underexplored work in applied AI is happening right now.

The PlaniN AI thesis — The gap between what foundation models can do in a lab and what they reliably deliver in a domain expert's workflow is enormous. Closing that gap — through layer optimisation, SME knowledge infusion, truth preservation and domain-specific model development — is where PlaniN's AI innovation is focused.

Where PlaniN Works in the AI Stack

User / Interface

Application Layer — Agents, workflows, domain tools, UI/UX for professional users

PlaniN Focus ★

Orchestration & Optimisation Layer — Context management, retrieval, verification, truth preservation, SME knowledge injection, hallucination control — this is our primary focus

PlaniN Focus ★

Model Layer — Domain-specific fine-tuning, small language models, efficient model growth from foundation & frontier models

Foundation

Foundation & Frontier Models — GPT, Gemini, Claude, Llama, Mistral and others — starting points we build on, not endpoints

Core AI Innovation Capabilities

What PlaniN builds and researches in AI

Research

Foundational & Frontier AI Research

We conduct original research at the foundational and frontier level — not commentary on what others are doing but original work on techniques, architectures and approaches that address gaps current models leave unresolved in professional and domain-expert contexts.

Core Focus

Layer Optimisation Between UI and Model

The orchestration layer — how context is built, how retrieval works, how outputs are verified and corrected before reaching the user — is where most real-world AI performance is won or lost. PlaniN's work here produces measurable improvements in accuracy, reliability and domain relevance.

SME AI

Diverse SME Knowledge Infusion

We bring deep subject matter expertise from planning, design, engineering, materials, urban systems, infrastructure and innovation into AI systems — making them genuinely useful for professional domain work, not just general question-answering. SME-infused AI is dramatically more useful and accurate in its domain.

Models

Efficient Domain-Specific Model Development

We grow domain-specific models from available foundation and frontier models — including small language models (SLMs) where they outperform larger general models on specific tasks. Our focus is efficiency: the smallest, fastest model that reliably performs the required task at professional-grade accuracy.

Novel

Truth Preservation & Zero Hallucination

Hallucination is not a minor inconvenience in planning, engineering and design — it is a professional liability. PlaniN is conducting deep research into truth preservation modes and zero-hallucination architectures that make AI outputs reliably verifiable. This work creates novel use cases in domains where AI was previously unusable due to accuracy requirements.

Enterprise

Workflow AI for Enterprises & Government

Bringing enterprise and government workflows into the AI domain in the most efficient way possible — identifying the highest-value workflow interventions, designing the AI integration architecture, and delivering the implementation with appropriate accuracy and governance controls for professional and public sector contexts.

Deep Research Focus

Truth Preservation &
Zero Hallucination AI

In planning, engineering, design and legal domains, a hallucinated output is not an inconvenience — it is a professional failure. Current foundation models hallucinate at rates that make them unsuitable for high-stakes domain work without extensive human verification.

PlaniN's deep research into truth preservation modes and zero-hallucination architectures is creating a new class of AI tools that are reliably verifiable, traceable and accurate — unlocking use cases in planning, engineering, legal and scientific domains that current AI simply cannot serve.

Verifiable

Every output traceable to a source — no unverifiable generation in high-stakes domains

Domain-Locked

Models constrained to operate within verified domain knowledge — preventing out-of-domain confabulation

Novel Use Cases

Truth-preserved AI enables engineering sign-off, legal reference, regulatory submission and medical protocol support — currently impossible with hallucinating models

Small Language Models — The Efficient Frontier

Domain-specific SLMs that outperform general models at a fraction of the cost

For many professional domain tasks, a well-trained small language model outperforms a large general model — and does so at dramatically lower cost, latency and computational requirement. PlaniN's SLM development programme grows purpose-built models for specific domain tasks using available foundation and frontier models as starting points.

Grow Your Own Model

PlaniN helps organisations grow their own efficient domain models from foundation or frontier starting points — using your proprietary data, your domain knowledge and our model development methodology. The result is a model that knows your domain, your terminology and your standards — and does not need a general-purpose model to function.

SLM vs LLM — When Small Wins

For tasks with well-defined scope, verified training data and accuracy requirements, SLMs consistently outperform LLMs on domain metrics while costing less to run and being faster to respond. PlaniN evaluates each use case to determine the optimal model size and architecture — not defaulting to the biggest available model.

Scope of AI Innovation Engagements

Click any area to expand

Foundational AI Research & Prototype Development +

Original research into AI techniques, architectures and approaches relevant to planning, design, engineering and innovation domains. Research outputs include working prototypes, technical papers and validated methodology that can be applied in production systems or licensed to partners.

AI Layer Optimisation for Existing Systems +

Audit and redesign of the orchestration layer in existing AI implementations — context window management, retrieval augmented generation architecture, prompt engineering at scale, output verification pipelines and hallucination reduction. Delivered as a measurable improvement in system accuracy and reliability.

Domain-Specific Model Development & Fine-Tuning +

End-to-end development of domain-specific language models — from training data curation and SME knowledge structuring through fine-tuning, evaluation and deployment. Covers both large model fine-tuning and small language model development depending on the use case requirements.

Enterprise & Government Workflow AI Integration +

Identification of highest-value AI workflow integration points in enterprise and government operations. Design of integration architecture with appropriate accuracy, governance and audit controls. Delivery and implementation of AI workflow tools that improve operational efficiency while maintaining the reliability standards that professional and public sector contexts require.

Truth Preservation & Verifiable AI for High-Stakes Domains +

Design and implementation of truth-preservation architectures for domains where hallucination is professionally unacceptable — engineering, planning, legal, medical and scientific contexts. Outputs include verifiable AI tools where every generated statement is traceable to a verified source within the domain knowledge base.