Building Products at the Intersection of Product, Technology, Data and AI
Hi, I’m Honey Srivastava, a Product and Technology Consultant with 11+ years of experience across software engineering, technical product management, B2B SaaS, data platforms, cloud-based systems and AI-enabled products.
My career started in software engineering, where I built applications, backend services, APIs and data-driven systems. Over time, I moved closer to the problems behind the technology—understanding customers, defining products, designing platforms and connecting technical decisions with measurable business outcomes.
Today, I work at the intersection of:
Product Strategy × System Design × Cloud Architecture × Data × Artificial Intelligence
This combination allows me to look beyond features and roadmaps.
I think about the complete product system: the business problem, customer journey, workflows, architecture, APIs, data, security, scalability, AI capabilities and the metrics that ultimately determine whether a product is successful.
What I Do
I help turn complex or ambiguous business problems into products and technology systems that can be designed, built, launched and measured.
My work typically spans the complete product journey:
Problem Discovery — What problem exists? For whom? Why does it matter?
Product Strategy — What outcome are we trying to achieve and what direction will we take?
Product Definition — Users, use cases, requirements, MVP, priorities.
Business & Technical Feasibility — Is it valuable, viable and technically realistic? This is where ROI/TCO/business case can sit.
Architecture — System design, services, APIs, cloud, integrations, security.
Data & AI Design — Data sources, pipelines, models/RAG, evaluation, guardrails.
Delivery — Backlog, development, testing, dependencies, releases.
Launch — Pilot/canary/rollout, training, support, go/no-go.
Measurement & Improvement — KPIs, monitoring, feedback, optimization.
Depending on the problem, this can involve:
- Product discovery and strategy
- Product roadmaps and PRDs
- Business and technical feasibility
- Cloud and product architecture
- System and service design
- API and integration architecture
- Data modelling and data pipelines
- AI and LLM product architecture
- Security, scalability and reliability requirements
- Agile product delivery
- Product metrics and experimentation
- Release readiness and post-launch optimization
I have applied this approach across LegalTech, B2B SaaS, CRM, analytics, conversational commerce, generative AI, search platforms and computer vision.
Selected Work & Impact
AI-Powered Legal Research
I currently work on an AI-powered legal research and judgment intelligence platform covering more than 1.18 million court judgments and Bare Acts.
My work spans product strategy, roadmap development, PRDs, release planning and product architecture.
I have helped shape an AI research assistant using Retrieval-Augmented Generation (RAG) with query interpretation, jurisdiction-aware retrieval, time-scoped search, source-grounded responses and guardrails designed for a high-stakes legal environment.
The platform architecture spans seven core services, including identity, subscriptions, entitlements, payments, document access, search and AI.
My work also includes defining API contracts, data flows, failure handling, PostgreSQL structures, search indexing, role-based access control, auditability and performance requirements.
Users reported approximately 60% faster legal research compared with traditional manual keyword searching.
Building Data Products for Better Decisions
I have worked on the definition of client-facing analytics products, including gold data layers, schemas, data-quality rules and downstream consumption requirements.
I have also built Python and SQL-based transformation pipelines supporting live users across multiple client environments.
In another engagement, I worked on customer segmentation using RFM analysis to identify high-value, loyal and at-risk customers and translate those segments into actionable engagement strategies.
These experiences reinforced an important principle in the way I approach data products:
A dashboard is only the visible layer. The real product begins with trusted data, clear definitions and a decision the business needs to make.
Generative AI Products
I have contributed to the modernization of CRM, data and generative AI capabilities for a B2B SaaS platform serving multi-site operators.
One of the products I helped deliver was an LLM-powered content generation suite with tone-of-voice matching, customer-data personalization and built-in split testing.
I also contributed to the next generation of a CRM platform and worked across member data flows, SQL-driven analytics, reporting services, third-party integrations and platform security.
The work included cloud-hosted services and considerations around authentication, authorization, encryption and protection of customer data.
Conversational Commerce That Produced Measurable Growth
I helped design and deliver an AI conversational commerce and customer-support platform operating across web and WhatsApp.
The assistant supported:
Product discovery
Order tracking
Returns
Customer support
Commerce-related conversations
The platform automated approximately 70% of inbound customer queries and contributed to a 12% A/B-tested uplift in sales.
Behind that customer experience was a broader architecture involving intent detection, API services, cloud infrastructure, relational databases, caching, webhooks, analytics and automated reporting.
This project reinforced something that continues to influence the way I approach product development:
The best technology often feels simple to the customer because the complexity has been solved behind the scenes.
Designing Computer Vision Around Business Risk
I have also worked on an automated computer-vision quality inspection platform for manufacturing.
Instead of evaluating the AI model using accuracy alone, we defined performance around precision, recall, F1 score and false rejection rate, because incorrect decisions directly affected factory throughput and scrap costs.
The solution used confidence-based routing:
High-confidence prediction → Automated decision
Low-confidence prediction → Human inspection
Human auditor feedback was then incorporated into the model-improvement process.
This experience strongly influenced the way I think about AI:
AI systems should be optimized around the consequences of their decisions—not simply around impressive model metrics.
Turning 300+ Social Metrics Into a Unified Data Product
I have worked on a B2B social-media analytics platform integrating data across:
Facebook, Instagram, X, LinkedIn and YouTube.
The platform processed more than 300 organic and paid social-media metrics into a consistent cross-network data model.
My work also covered automated reporting across PPT, PDF, CSV, Slack and email, as well as predictive capabilities such as optimal posting time, posting-frequency forecasting and competitor benchmarking.
The challenge wasn’t simply collecting more data.
It was turning fragmented data from multiple platforms into consistent information that customers could understand and act upon.
From Software Engineer to Product & Technology Consultant
Before moving deeper into product and technology consulting, I spent several years in software engineering and technical leadership.
I built customer-facing applications, backend services, REST APIs and data-driven systems using technologies including PHP, JavaScript and MySQL.
My responsibilities also included technical design, estimation, code reviews, performance improvement, production troubleshooting and release execution.
That engineering foundation remains one of my strongest advantages in product work.
It allows me to move comfortably between conversations about:
Customer problems
and
APIs, databases, architecture, cloud infrastructure, security, scalability and engineering trade-offs.
I understand what happens after a requirement reaches engineering, which helps me shape products that are not only desirable but also technically realistic and scalable.
My Technical Perspective
I am particularly interested in the architecture underneath modern digital and AI products.
My work and learning span areas such as:
Cloud & Product Architecture
Cloud-native product architecture, service-oriented systems, service boundaries, authentication and authorization, scalability, reliability, security, observability and failure handling.
APIs & Integration Architecture
REST APIs, API contracts, third-party integrations, webhooks, identity, payments, subscriptions, entitlements and data exchange between systems.
Data Architecture & Platforms
SQL, PostgreSQL, MySQL, Elasticsearch, data modelling, data pipelines, data quality, analytics workflows, BigQuery, Power BI and Looker Studio.
AI & LLM Architecture
Generative AI, Large Language Models, Retrieval-Augmented Generation, embeddings, vector search, source grounding, AI evaluation, guardrails and human-in-the-loop systems.
Cloud & Engineering
Working knowledge across AWS, Azure and GCP, along with Python, Docker, backend systems, APIs and modern cloud-based product architectures.
I don’t believe a Product Leader needs to write every line of code.
But for technology products, understanding how the system works underneath the interface leads to better product decisions.
Education & Executive Learning
Technology gave me my foundation.
Product, business and leadership education helped me broaden it.
I hold an MCA from Guru Gobind Singh Indraprastha University and a B.Sc. in Information Technology from IGNOU.
I later pursued executive and professional education focused on product management, business and leadership.
Indian School of Business — ISB
Product Management Programme
Score: 100%
Indian Institute of Management Lucknow — IIM Lucknow
Sales & Marketing Leadership Programme
Score: 81.54%
Airtribe
AI-First Product Management
Score: 93
My professional credentials also include:
Certified Scrum Product Owner — CSPO
Advanced Certified Scrum Product Owner — A-CSPO
Aha! Product Management
I see certifications and executive education as frameworks—not substitutes for experience.
Their real value comes from combining structured thinking with years of actually building, launching and improving technology products.
Building Beyond the Job
I also enjoy building communities and sharing what I learn.
I founded a Product and AI community that grew to more than 1,500 members within three months and received coverage in national media.
Building that community reinforced something I believe strongly:
Knowledge becomes more valuable when people can exchange it, challenge it and build on it together.
How I Think About Products
Technology changes quickly.
The fundamental problems businesses need to solve usually don’t.
Organizations still need to:
Understand customers better.
Reduce friction.
Increase efficiency.
Make better decisions.
Grow revenue.
Manage risk.
Create experiences people value.
AI, cloud platforms, APIs, automation and data systems are powerful tools.
But technology itself is rarely the objective.
The objective is the business or customer outcome that technology enables.
That is why my approach normally begins with:
What problem are we solving?
Then:
What outcome should change?
And only then:
What product, data, architecture or AI capability should we build to make that happen?
What I’m Exploring Now
I am particularly interested in products where AI, data, cloud architecture and platform thinking intersect with real business problems.
That includes:
AI-enabled products
Intelligent search and retrieval
Data products and decision systems
Cloud-based SaaS platforms
Workflow automation
AI agents and intelligent workflows
Enterprise platforms and integrations
Product and system architecture
My goal is not simply to build AI products.
It is to build useful, scalable and commercially meaningful products—and use AI when it genuinely makes those products better.