For founders and product teams
A senior, PhD-led AI team you can’t yet afford to hire — agents, RAG, recommendations and full product builds, engineered for production from day one.
100+
projects delivered
20+
clients · North America, Europe & Asia
5.0
average client rating
PhD
AI-led delivery, every project
Weeks
not months, to a working pilot
Your AI co-builder
Grounded answers, evaluation suites and guardrails come standard — the engineering that separates a demo from a product.
The problem: You promised AI in the pitch and need it live before the next raise.
What we build: Agents, copilots, RAG search or recommendation features plugged into your product, with evals in CI.
Proof: Multi-agent recommender for a Singapore travel-tech startup
The problem: Traction metrics are scattered across Stripe, your database, HubSpot and spreadsheets.
What we build: A metrics layer and dashboard for traction, retention and cohorts — plus natural-language questions over it.
Proof: Insights platform for a US eye-care technology company
The problem: No full-time engineering team, or a team stretched too thin.
What we build: Web apps, role-based portals and APIs built by a senior pod amplified by our own AI agents.
Proof: Role-based fund portal for a US charitable foundation
The problem: Unsure which AI bets to make, which models to use, or which vendors to trust.
What we build: A few hours a month of PhD-level architecture review, roadmap and hiring support.
Proof: Lead engine built from scratch for a US B2B marketing-tech startup
Case study · Multi-agent recommendations
Singapore · Travel & tourism
Travellers needed destination suggestions that fit their persona — adventure, business, nature, family or couples — drawn from a large body of travel knowledge, fast enough and cheap enough to run at scale.
Lower cost & latency
Personalised recommendations running in production at scale. [Add % cost or latency reduction]
“Shiney and I have been working together on a variety of projects for over a year, and we’re just getting started!”
How we work
01 · Free
We look at how work moves today and name the one task worth automating first.
02 · 2–4 weeks
A working slice on your real data, with one number agreed up front to judge it by.
03 · Go live
Guardrails, cited answers and human approval on every high-stakes step.
04 · Ongoing
Monitoring, fixes and upgrades on a monthly plan. You own the code — no lock-in.
Results
14 projects across 11 industries and 6 countries. Client names stay confidential — each project is described by the type of organisation, the problem and what we built.
Most relevant to Startups shown first
Travel-tech startup · Singapore
Personalised recommendations in production, with cost and latency cut at scale through prompt caching, model right-sizing and batch processing.
Charitable foundation (small non-profit) · USA
Live and running in production for all three user roles.
Eye-care technology company (small business) · USA
Manufacturers get grounded, demographic-level insight into how their products are accepted.
Eye-care technology company (small business) · USA
Detailed trend and sentiment insight on ophthalmology products and brands.
Longevity-health startup · China
Pilot delivered with 100% precision on generated answers and no hallucinations; validated for production development.
B2B marketing-tech startup · USA
B2B lead generation and marketing running at scale — designed and built from the ground up.
“She designed and built our lead generation system from the ground up.”
“Her thoughtful approach to requirements and deep knowledge of NLP have been invaluable.”
“She built an exceptional conversational agent for a very complex project. Thoroughly impressed with the quality and speed of her work.”
Who you'll work with
Dr. Shiney Jeyaraj holds a PhD in AI, has published with IEEE and Elsevier, and is a Top Rated Upwork freelancer with a 100% Job Success Score. She leads every SharkAI project personally, with a delivery team amplified by our own AI agents — senior expertise at a cost that works for small teams.
AI Perspectives
AI Strategy ·
Most enterprise AI projects fail when moving from demo to production. Learn how Intelligent Triage, Semantic Caching, and Parallelized Retrieval keep your AI scalable, fast, and cost-predictable.
Read article →Industry Insights ·
Key takeaways from the India AI Impact Summit 2026 and why Indian organizations must move from AI experimentation to building sovereign, vertical AI systems.
Read article →Technical Deep Dive ·
How we architected a stateful RAG system that fuses official IRS data with real-time web intelligence to create a verified truth engine for enterprise discovery.
Read article →You do — all of it, documented, in your repositories. No black boxes and no lock-in.
Retrieval grounded in your data with citations, golden-set evaluations in CI, guardrail checks, and the model abstaining when the answer isn’t in the source.
Yes — project-based, staff augmentation, or an ongoing partnership, whichever fits your stage.
Python, FastAPI, Django, React; AWS (Bedrock, Lambda, Glue, Neptune), Azure OpenAI, Claude, Gemini and open-source models.
Book a free 30-minute AI review. You'll leave with one task worth automating first — whether or not you work with us.
