Generative AI & LLM Engineering
RAG pipelines, agentic workflows, knowledge graphs, and multi-step reasoning systems built with LangChain, LangGraph, and vector databases — engineered for accuracy and production reliability, not demos.
AI & ML Engineering Consultancy
Thinkvia AI Solutions Inc. partners with companies on a project basis to design, build, and deploy Generative AI applications, machine learning systems, and MLOps pipelines — plus technical screening interviews and ad-hoc AI evaluations for teams that need an expert, fast.
What We Do
Engagements are project-based and scoped to your goals — from a two-week evaluation sprint to a full production build.
RAG pipelines, agentic workflows, knowledge graphs, and multi-step reasoning systems built with LangChain, LangGraph, and vector databases — engineered for accuracy and production reliability, not demos.
End-to-end predictive modeling, experimentation design (A/B & multi-arm bandit), and deep learning systems that drive measurable business outcomes across marketing, risk, and product decisions.
Autonomous agents and automation pipelines that standardize ML workflows, orchestrate deployments, and validate model performance against production baselines — cutting development and deployment time dramatically.
Need a fast, unbiased read on whether your AI system actually works? We design evaluation frameworks, benchmark LLM/agent outputs, stress-test RAG pipelines, and run focused ad-hoc AI projects on short timelines.
Hiring for AI/ML roles? We conduct expert technical screening interviews — assessing candidates on ML fundamentals, LLM/GenAI engineering, system design, and hands-on coding — so you hire with confidence.
Data pipelines, executive dashboards, and governance built on BigQuery, Looker, Power BI, and dbt — turning raw data into decisions your teams can act on weekly.
Cut token spend and latency on production LLM systems using Contextpress, our in-house context-compression engine — without sacrificing output quality. Built for teams running RAG or agents at scale.
Our Approach
Every engagement follows the same operating principles — regardless of scope.
You work directly with the engineer building your system — not an account manager routing requests to a rotating team.
Systems are validated against real baselines and shipped to run reliably in production — not left as unmaintained notebooks or demos.
Every GenAI system we ship is measured against clear quality metrics before it reaches your users, not after.
Engagements are priced and timelined to the problem — a focused sprint, a pilot, or a full production build.
Selected Work
Representative engagements, anonymized for client confidentiality.
Delivered a flexible GUI platform with LLM-powered nodes supporting advanced RAG pipelines, configurable agent logic, and coding nodes for designing and deploying banking workflows — driven by metrics-based optimization cycles across stakeholder groups.
Designed and shipped a context-aware chatbot for a regulatory reporting function, advancing the RAG pipeline with knowledge graphs and structured-output validation. Agentic workflows automated complex reporting logic, cutting repetitive developer workload by 50%.
Engineered an autonomous agent that orchestrated end-to-end deployment pipelines and validated model performance against baselines. A companion code-refactoring agent standardized the ML workflow across the team — accelerating development speed by 150% and cutting deployment time by 250%.
Built a standardized A/B testing framework spanning program, category, and customer-level experiments, with predictive models and automated monitoring feeding executive dashboards for weekly decision-making.
How We Work
A short discovery call to define the problem, success metrics, and timeline — fixed scope or ongoing retainer.
Rapid, iterative development with regular check-ins — prototypes validated early, engineered for production from day one.
Rigorous testing against baselines, evaluation metrics for AI/LLM quality, and documentation your team can own.
Production deployment, knowledge transfer, and optional ongoing support or technical interviewing for your growing team.
Let's Talk
Whether it's a full GenAI build, an ML system, a quick AI evaluation, or a technical interview panel — reach out and let's scope it together.