Consulting with Baljit Singh

Fractional AI architect for memory-native LLMs, symbolic RAG, and agentic AI systems.

I help founders, CTOs, enterprise R&D teams, and AI infrastructure groups design reliable AI systems that go beyond commodity chatbot and RAG implementations.

My work sits at the intersection of LLM architecture, long-term memory, neuro-symbolic systems, multi-agent workflows, thought representation, and hardware-aware design. Available for selective part-time consulting, typically 40 to 80 hours per month, with US and global clients.

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Selective availability: 40 to 80 hours per month • Remote with US and global teams • Architecture reviews, technical sprints, and advisory retainers

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Who I help

Teams facing a difficult architecture question.

I work best with teams that already understand AI is not only a model selection problem. The best fit is a team facing a hard architecture question where memory, structure, reliability, correction, or hardware constraints matter.

Founders

AI startup founders

For founders building differentiated AI products and needing a deeper architecture point of view before scaling.

CTOs

CTOs and engineering leaders

For teams that need an outside review of RAG, agent workflows, memory systems, evaluation, and production risks.

Enterprise

Enterprise R&D teams

For groups prototyping future-facing AI systems where explainability, auditability, domain knowledge, and workflow fit matter.

Investors

Investors and venture teams

For technical diligence on whether an AI product has real architecture depth or is mostly a thin wrapper.

Problems

Architecture problems I help teams think through.

The conversations I find most useful start with a specific failure mode, constraint, or decision point — not a vague AI roadmap.

RAG systems that work in demos but fail on complex, long, or narrative knowledge.
AI agents that need memory, supervision, escalation, and correction loops.
LLM systems that need persistent, editable, auditable memory.
Knowledge systems that need symbolic structure, provenance, and better retrieval behavior.
Healthcare or domain-specific AI systems that need explainability and minimal-data learning patterns.
AI products where latency, cost, memory bandwidth, edge constraints, or hardware tradeoffs matter.
Technical roadmaps where the team needs a differentiated architecture, not only a model wrapper.

Engagement formats

Three ways to work together.

Each engagement is shaped around a real architecture question. I keep the formats deliberately small so the work stays focused.

Diagnostic

AI Architecture Diagnostic

A focused review before you scale, rebuild, or raise.

A short engagement to review your current AI architecture, identify failure modes, and produce a practical roadmap. Useful when a prototype is promising but the team is unsure how to improve reliability, memory, retrieval quality, cost, or evaluation.

Typical scope: architecture review, RAG/agent/memory/evaluation assessment, failure-mode map, cost & reliability concerns, technical roadmap, working session with founder, CTO, or engineering team.

Best for: seed to Series B AI startups; CTOs preparing to scale; teams deciding whether to rebuild or refine.

Typical duration: 2 to 3 weeks.

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Sprint

Memory-Native LLM & Symbolic RAG Sprint

For teams that need better memory, retrieval, provenance, and reasoning structure.

A focused design sprint for AI systems where plain vector search and prompt orchestration are not enough. Defines the memory model, symbolic structure, retrieval strategy, correction loop, and evaluation approach.

Typical scope: memory architecture, symbolic retrieval design, provenance and audit model, thought representation, human review and correction flow, prototype plan, evaluation criteria.

Best for: complex knowledge products; enterprise search and knowledge assistants; healthcare, legal, technical, or operational knowledge systems; long-conversation or multi-step workflows.

Typical duration: 4 to 6 weeks.

Discuss a memory or RAG sprint →

Retainer

Fractional AI Architect

Senior architecture support without a full-time executive hire.

Ongoing advisory for founders, CTOs, and advanced teams that need senior AI architecture judgment while building. Can include architecture reviews, technical roadmap design, prototype checkpoints, research direction, vendor or model tradeoff reviews, and team guidance.

Typical scope: 40 to 80 hours per month, architecture reviews and working sessions, technical roadmap and decision support, RAG / agent / memory / evaluation guidance, research-to-product translation, support for founder or senior engineering team.

Best for: founders building technically differentiated AI products; teams without a full-time senior AI architect; companies that need deep review at key product checkpoints.

Typical structure: monthly advisory retainer.

Explore fractional support →

Advisory areas

Additional areas where I can help.

Neuro-symbolic AI and explainable pipelines.
Multi-agent systems with shared memory and corrective loops.
AI reliability, evaluation, and failure-mode analysis.
Healthcare AI architecture and medical workflow understanding.
Streaming NLU, speech systems, and language understanding architecture.
Hardware-software co-design for AI acceleration.
Neuromorphic, analog, and noise-tolerant AI directions.
Technical diligence for investors and strategic partners.

Healthcare-related consulting is focused on AI architecture, workflow design, data structure, reliability, and technical review. It is not medical advice.

Why me

Why my perspective is different.

Most AI consulting starts from models and tools. My perspective starts from architecture: memory, representation, correction, topology, and the compute substrate. I have built across language understanding, thought representation, neuro-symbolic systems, medical speech, visual context enrichment, spiking neural simulation, hardware development, and chip design. That range helps me see where AI systems fail when they leave the demo environment and enter real workflows.

Proof

Track record

Creator of GIPCA and BISLU direction.
Work on thought representation and ETML.
Multiple US and India patents in AI architecture, thought representation, healthcare AI, and medical record analysis.
Experience across deep learning, LLMs, neuro-symbolic AI, speech, hardware development, and chip design.
Founder of Mankash AI Labs.

Range

One practical view across the stack

The differentiator is not just one model or one product category — it is the ability to connect language, AI architecture, memory, hardware, and execution into one practical view.

How to start

Send a short note about what you are building.

The best first conversation is not a sales call. It is a focused discussion about the constraints, failure modes, and decision points that matter.

What are you building?
What stage is the product or prototype in?
What is not working or not clear yet?
What matters most: quality, memory, latency, cost, reliability, explainability, privacy, or scale?
Are you looking for a diagnostic, sprint, retainer, or investor diligence review?

Consulting can be structured through Mankash AI Labs and affiliated entities depending on client procurement and compliance needs. Engagement structure can be discussed based on client procurement requirements and professional advice from the relevant legal and tax advisors.