AI Startup CTO Advisory & GenAI Architecture

AI Product Engineering for Startups
Building on LLMs That Need to
Work in Production

GenAI architecture, RAG pipeline design, LLMOps, and AI product engineering for AI-first startups that need systems that are reliable, cost-controlled, and actually measurable.

GenAI · RAG · LLMOps 20+ Years Technology Leadership Architecture · Evaluation · Production
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20+Years Technology Leadership
GenAI& LLM Architecture
RAG Pipelines& Vector Systems
LLMOps& Eval Frameworks
AI ProductEngineering & Delivery
AI DueDiligence & Review
The Reality

What Breaks First
in AI Product Engineering

Most AI startups don't fail because the model isn't good enough. They fail because the system around the model wasn't designed for production. Evaluation gaps, cost surprises, hallucination rates that only surface at scale, and architecture decisions that made sense in a demo but don't survive real user behaviour.

No evaluation framework before building the pipeline
The most common AI product mistake: building the full RAG or agentic pipeline before defining what correct output looks like. Without a golden eval dataset and measurable quality criteria, you're optimising by feel. Model upgrades, prompt changes, and retrieval tuning have no reliable way to be validated.
Token costs that aren't modelled before launch
A RAG pipeline that costs $0.08 per query with 200 beta users costs $800/day at 10,000 users. Most AI startups discover their unit economics problem after launch, not before. Token cost modelling at realistic usage volumes is engineering work, not a finance concern.
Hallucinations that are acceptable in demo, not in production
LLM hallucinations in a demo context are forgiven. In a production system handling customer queries, legal documents, or medical information, they're business and liability risks. Most teams don't have guardrail layers, output validation, or human escalation paths designed before the first real user touches the product.
RAG retrieval that degrades at scale
A retrieval pipeline that works with 10,000 documents often degrades significantly at 1M. Chunking strategy, embedding model choice, vector index configuration, and re-ranking logic all affect retrieval quality in ways that aren't visible in small-scale testing but become the primary source of output quality problems in production.
Provider lock-in with no abstraction layer
Application code that directly calls gpt-4o or claude-3-5-sonnet API endpoints isn't architecturally wrong - until OpenAI changes pricing, a new model requires prompt rewrites, or a customer requires a self-hosted model for data residency. The abstraction cost is low early; the migration cost is high late.
No observability into what the LLM is actually doing
If you can't trace a bad user interaction back to the specific prompt, context window, and model response that caused it, you can't fix it reliably. LLM applications need tracing at the request level - every prompt, every retrieval result, every model output - so debugging is deterministic, not probabilistic.
User feedback that isn't integrated into evaluation
User thumbs-up and thumbs-down signals are the highest-signal eval data available for production AI systems. Most teams collect this data and don't use it. It should feed directly into the eval dataset, inform prompt improvement cycles, and flag output categories where the model is systematically underperforming.
No content safety or guardrail layer
LLMs will produce unexpected outputs when users push boundaries - intentionally or not. Prompt injection, jailbreaking, and off-topic generation are real surface areas in production AI applications. Building guardrails as an afterthought is significantly more expensive than designing them in from the start.
Prompts deployed without version control or testing
A prompt is production code. Changing it without versioning, without eval coverage, and without a rollback mechanism is the AI equivalent of deploying untested code directly to production. Most AI teams don't treat prompts this way until a prompt change causes a customer-visible regression they can't easily reverse.
Agentic systems with no control or failure handling
Multi-step AI agents that can write code, call APIs, or modify data are powerful and inherently risky. Without tool call validation, permission boundaries, loop detection, and graceful failure handling, an agentic system that works 95% of the time causes disproportionate damage in the 5% where it doesn't.
Advisory Areas

What I Help AI Startups
Get Right

GenAI Architecture & System Design
Designing the full AI application stack - model selection, retrieval architecture, context management, orchestration patterns, and the infrastructure decisions that determine whether the system is maintainable and cost-efficient at production scale.
RAG Pipeline Design & Optimisation
Building retrieval pipelines that work at scale - chunking strategy, embedding model selection, vector database configuration, hybrid search, and re-ranking. Diagnosing why retrieval quality degrades and what the structural fixes are rather than iterating on hyperparameters that don't address root cause.
Evaluation Framework Design
Defining what good looks like for your specific AI use case. Building golden datasets, automated eval pipelines, human evaluation workflows, and regression testing that catches quality degradation when models, prompts, or retrieval logic change.
LLMOps & Production Tooling
Prompt versioning, request tracing, cost monitoring per feature, latency dashboards, A/B testing for models and prompts, and guardrail monitoring. The operational layer that lets a small team manage an AI application in production without constant manual intervention.
Fine-Tuning vs RAG vs Prompting Decision
Structuring the build-vs-buy-vs-fine-tune decision for each AI feature. Most teams reach for fine-tuning too early. Most prompting strategies have more headroom than teams realise. Getting this architecture decision right before spending engineering time and training costs is high-leverage work.
AI Infrastructure & Cost Architecture
Token cost modelling at realistic usage volumes, caching strategies, context compression, batch inference design, and self-hosted vs API trade-offs. Building cost monitoring from the start and designing for the unit economics that make the business sustainable.
Agentic System Architecture
Designing multi-step AI agents with appropriate control mechanisms - tool call boundaries, permission models, loop detection, failure handling, and human escalation paths. Building agentic systems that are both capable and safe to deploy to real users.
AI Technical Due Diligence
Independent review of AI product architecture for investors and acquirers. Covering model selection rationale, retrieval quality, evaluation coverage, cost structure, safety posture, and whether the technical approach supports the business thesis at scale.
GenAI Architecture

Building an AI Application That
Holds Up in Production

The gap between an AI demo and a production AI system is larger than most teams expect. The model is rarely the bottleneck. The retrieval quality, the evaluation coverage, the cost structure, the guardrail layer, and the operational tooling are where most production AI systems fall short - not because teams don't know these things matter, but because building the product felt more urgent than building the system.

01
Evaluation Framework First
Before building the pipeline, define what correct output looks like. Create a golden dataset of representative inputs and expected outputs. Define measurable quality criteria: factual accuracy, format adherence, relevance, safety. Build automated evals that run on every model change, prompt change, and retrieval change. Without this, every improvement is anecdotal and every regression goes undetected until a user reports it.
02
Retrieval Architecture & Indexing
RAG quality is 60% retrieval, 40% generation. Chunking strategy - size, overlap, semantic vs fixed - determines whether the right context gets retrieved. Embedding model selection affects recall quality. Hybrid search (dense + sparse) often outperforms pure vector search for domain-specific content. Re-ranking with a cross-encoder improves precision at the top of the result set. These aren't optimisation details; they're the primary determinants of output quality.
03
Prompt Engineering & Version Control
Prompts are production code. They need version control, eval coverage before deployment, and rollback capability. System prompt architecture - separating persona, context injection, formatting instructions, and safety constraints - makes prompts maintainable as requirements evolve. Chain-of-thought, few-shot examples, and output format specification are high-leverage prompt techniques that most teams underuse.
04
Cost Architecture & Token Optimisation
Model token costs at scale are a business variable, not a technical detail. Context compression - summarising conversation history, trimming retrieved context to relevance - can reduce token costs 40-60% with no quality degradation. Semantic caching for repeated or similar queries reduces API calls. Per-feature cost monitoring gives visibility into which product features are economically sustainable before they become a problem at user scale.
05
Guardrails & Safety Layer
Input validation, output filtering, content safety classification, and prompt injection detection are not optional features. They're the difference between a production AI system and a liability. The guardrail layer should run independently of the primary inference path - so safety checks don't add to latency for clean requests, but do intercept problematic inputs and outputs before they reach users or downstream systems.
06
LLMOps & Observability
Every production LLM request should be traceable: input prompt, retrieved context, model response, latency, token count, cost, and user feedback signal. This trace is the operational foundation for diagnosing quality problems, optimising costs, and running controlled experiments. LLM-specific tooling - LangSmith, Helicone, or Arize - provides this without building it from scratch. Without traceability, debugging AI application problems is guesswork.
AI CTO Competencies

AI Leadership Areas &
Advisory I Deliver

AI startup challenges aren't just technical - they're strategic. Which architecture to bet on. Whether to build or buy. How to measure quality before investors or customers measure it for you. The most valuable CTO contribution to an AI startup is often the decision framework, not the implementation.

AI Product Strategy
Build vs Buy LLMRAG vs Fine-Tuning DecisionModel Selection StrategyCost ArchitectureAI Feature PrioritisationGo-to-Market Technical FitAI Differentiation Strategy
AI Architecture & Design
RAG Pipeline ArchitectureAgentic System DesignContext Management StrategyRetrieval Quality DesignPrompt Engineering StrategyModel Abstraction LayerAI Infrastructure Design
Quality & Evaluation
Eval Framework DesignGolden Dataset CreationHallucination ManagementOutput ValidationRegression Testing for AIQuality Metrics DefinitionHuman Eval Workflow
LLMOps & Production
Prompt VersioningCost Monitoring per FeatureLatency TrackingA/B Testing for ModelsModel Upgrade ManagementRequest TracingUser Feedback Integration
AI Safety & Risk
Content Safety DesignPrompt Injection DefenseOutput FilteringGuardrail ArchitectureBias AssessmentData Privacy in AIResponsible AI Framework
AI Technical Due Diligence
Architecture Defensibility ReviewEvaluation Coverage AuditCost Sustainability AnalysisTeam Depth AssessmentPlatform Risk AnalysisAI Moat AssessmentSeries A / B Pre-Raise Review
AI Leadership & Advisory
AI Technical RoadmapInvestor Relations SupportModel StrategyData StrategyAI Ethics & GovernanceEngineering Team for AIAI Vendor Evaluation
Production AI

Taking an AI Product from
Demo to Production

Most AI products live in a permanent demo-to-production gap. The prototype works. The architecture review reveals the production gaps. The team knows they need evals, guardrails, and observability - but the product roadmap keeps moving before the infrastructure catches up.

The approach I use is pragmatic: identify which production gaps create actual business or quality risk at your current user scale, and close those first. You don't need a fully mature LLMOps stack at 500 users. You do need it before you 10x.

The goal is a production AI system that a small team can operate confidently - where quality regressions are caught before users see them, costs are understood and controlled, and failures are diagnosable rather than mysterious.

  • Output quality is measurable, not just impressionistic
  • Model and prompt changes can be validated before deployment
  • Token costs are understood per feature and per user segment
  • Bad outputs are traceable to their root cause in minutes
  • Retrieval quality doesn't degrade as the knowledge base grows
  • Guardrails catch safety and content issues before users see them
  • User feedback feeds back into evaluation and improvement cycles
  • The architecture isn't locked to a single provider or model family
Beyond the Obvious

AI Application Areas
Worth Getting Right

The highest-value AI applications aren't always the most visible ones. These are the areas where thoughtful architecture design separates AI products that compound in quality over time from ones that plateau or degrade.

Semantic Search & Knowledge Retrieval
Enterprise knowledge search, documentation retrieval, and internal tool search built on vector embeddings rather than keyword matching. The quality delta between well-designed and poorly-designed RAG retrieval is large and directly user-visible.
Domain-Specific AI Assistants
AI assistants grounded in proprietary domain knowledge - legal, medical, financial, technical - where hallucination cost is high and factual accuracy is the product differentiator. Retrieval quality, source citation, and confidence calibration are the architectural decisions that matter.
Document Processing & Extraction
LLM-powered extraction of structured data from unstructured documents - contracts, invoices, reports, forms. The architecture decisions around chunking, schema validation, and extraction confidence thresholds determine whether the pipeline is production-reliable or prototype-quality.
Agentic Workflow Automation
Multi-step AI agents that can research, reason, and execute actions across tools and systems. The challenge isn't building agentic capability - it's designing the control, permission, and failure-handling boundaries that make agents deployable to real users at scale.
AI-Assisted Developer Tooling
Code generation, review automation, test generation, and documentation tooling built for internal engineering teams. The ROI on well-implemented developer AI tooling for small engineering teams is high - but the implementation quality determines whether adoption sticks.
Who This Is For

The Teams That
Reach Out to Me

AI-First Startup Founders
You're building a product where AI is the core value proposition - not a feature added to an existing product. You need architecture guidance that's specific to the AI application layer: evaluation, retrieval, cost, guardrails, and the operational stack that keeps it working after launch.
Founders Adding AI to Existing Products
You have a working product. You're adding AI features - customer support automation, document processing, internal search, or a natural language interface. You need an architecture that integrates cleanly with what exists and doesn't create a parallel system that's expensive to maintain.
Engineering Leads Inheriting AI Systems
You've joined a team with an AI product that's live but fragile. No evals, no observability, prompts deployed manually, costs not monitored. You need a pragmatic plan for what to fix first and what the production-ready version of this system looks like.
Investors Evaluating AI Startups
Technical due diligence for AI startup investments. An independent read on whether the AI architecture is defensible, whether the evaluation coverage is real, whether the cost structure is sustainable, and whether the team has the technical depth to execute on the AI thesis.
Why This Matters

I Understand AI Product
Complexity From Production

AI application engineering is a new discipline with a fast-growing set of best practices - and a large collection of patterns that look right in tutorials but fail in production. The hallucination handling that works on a demo dataset, the RAG pipeline that degrades at scale, the token cost model that wasn't run at realistic usage volumes - these are production failure modes, not theoretical ones.

I bring the perspective of someone who has designed large-scale production systems for 20 years - applied to the specific challenges of AI application engineering. The architectural discipline that makes distributed systems reliable is the same discipline that makes AI systems reliable. The tools are new; the principles are not.

20+
Years in Technology Leadership
31+
Products Shipped to Production
35M+
API calls/day at peak scale
India & UAE
& International Markets
FAQ

Common Questions

We're building an AI-native product. Where do we start on architecture?
Start with the evaluation framework before the architecture. Before choosing between RAG vs fine-tuning, hosted vs self-hosted, OpenAI vs open source - define what good output looks like for your use case, how you'll measure it, and what latency and cost constraints you're designing for. Most AI product architecture mistakes happen because these questions weren't answered first.
When should we fine-tune a model versus using RAG?
RAG is the right starting point for most AI product use cases. It's faster to iterate, cheaper to maintain, and doesn't require training data you may not have. Fine-tuning makes sense when you need consistent output format, domain-specific tone that prompting can't achieve, or latency constraints that make RAG retrieval impractical. Most teams reach for fine-tuning too early, before they've exhausted what's achievable with well-designed RAG and prompting.
How do we evaluate LLM output quality systematically?
You need an eval framework before you need an LLM pipeline. Define what correct looks like for your use case - factual accuracy, format adherence, relevance, safety. Build a golden dataset of expected inputs and outputs. Run evals on every model change, prompt change, and retrieval change. Without this, you're optimising by feel, and feel doesn't catch regressions before users do.
What does LLMOps actually involve for an early-stage AI startup?
At minimum: prompt versioning, eval pipelines, cost monitoring per request, latency tracking, and a way to trace failures back to specific prompt and context combinations. As you scale: A/B testing for prompts and models, user feedback integration into evals, guardrail monitoring, and model upgrade management. Most early-stage teams have none of these, which means they're flying blind on quality and cost.
We're using OpenAI. Should we be building model-agnostic?
Yes, with caveats. You don't need to abstract everything on day one. But your application code shouldn't contain direct references to specific model names or provider-specific SDK patterns if you can help it. A thin abstraction layer means a model migration is a configuration change rather than a codebase refactor. The cost of this abstraction early is very low; the cost later is very high - especially when a model gets deprecated or a better option emerges.
How do we manage AI infrastructure costs as we scale?
The two biggest cost levers are context window size and call volume. Most teams pass too much context per request and make redundant API calls. Caching at the embedding level, semantic caching for repeated queries, and context compression before LLM calls can reduce token costs by 40-70%. For self-hosted models, GPU utilisation and batch inference scheduling are the primary levers. Cost monitoring per user, per feature, and per request type is essential before scaling.
Our RAG pipeline quality is inconsistent. What are the most common root causes?
The most common root causes in order of frequency: chunking strategy that splits semantic units across chunks so the right information is never fully retrieved; embedding model mismatch where the model used at query time differs from the one used at indexing time; top-k retrieval returning noise at higher k values without re-ranking to improve precision; and context window stuffing where retrieved chunks exceed what the LLM can effectively use. Each has a different fix - which is why diagnosing before iterating matters.
Can you help with AI technical due diligence for an investment?
Yes. AI startup due diligence involves questions that general technical reviewers often miss: Is the evaluation coverage real or superficial? Is the RAG architecture designed to scale? Is the cost structure sustainable at 10x users? Is there genuine model defensibility or just a thin wrapper? Does the team have the depth to maintain and improve the AI system as model capabilities evolve? I can provide an independent technical review that answers these questions clearly.
What's the best way to start?
Book a 30-minute session using the calendar on the strategy page. Come with one specific challenge - architecture decision, eval framework design, retrieval quality problem, LLMOps tooling, or AI product strategy. The more specific the problem, the more useful the conversation.

Ready to Talk Through Your
AI Product Architecture? Let's Go.

30 minutes. No pitch. A direct conversation about your AI stack, your production gaps, and what's actually worth fixing right now.

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