Insight Origin

AI PRODUCT ENGINEERING STUDIO

From AI proof-of-concept to production system that actually works.

Most teams can build an AI demo in a weekend. Far fewer can ship one that survives real users, real scale, and real edge cases. Insight Origin closes that gap - RAG systems, AI agents, evaluation, APIs, and data platforms, designed, built, and shipped as complete products.

Book a 30-minute call →Start with a reliability audit →
  • 12+ years in production systems
  • 30+ shipped projects
  • Production systems on AWS, Azure & GCP
  • Clients across the US, UK & EU

THE PROBLEM

The gap between a demo and a system

Your team built an AI prototype. It worked in the demo. Then it met production - concurrent users, messy real-world data, cost ceilings, edge cases nobody scoped - and stalled.

This is the most common place AI projects die: not in research, but in the 0 → 1 crossing from “it works on my machine” to “it works for the business.” That crossing is engineering, architecture, and judgment.

Whether you’re taking a first system to production or rescuing one that stalled after the demo, that crossing is the work.

WAYS TO WORK TOGETHER

Three ways in

AI Reliability Audit

Fixed price · 1-2 weeks

Your LLM feature works in the demo, but you can’t tell how often it’s wrong in production. We review prompts, retrieval, tool use, and a sample of real outputs, rank the failure modes by impact, and hand you a prioritized fix list with effort estimates.

Best for: a team with an AI feature that mostly works.

Prototype to Production

Milestone-based · typically 4-12 weeks

Your prototype stalled before real users. We add what production needs: evaluation suites, guardrails and structured outputs, observability, cost control, and an architecture that holds up at scale.

Best for: a team whose demo works and whose system doesn’t.

Product Build, 0 → 1

Milestone-based · scoped per product

You have the idea and the customers, not the engineering team. We run discovery, design the architecture, and build and launch the product, bringing in senior specialists as the project needs them.

Best for: founders without an in-house engineering team.

Every engagement starts with a 30-minute call and a written scope. Book the call →

Selected work

nGAGE Talent (UK recruitment)

LLM agent over a deterministic rules engine

Built a public chat assistant that turns a plain-English hiring scenario into an indicative employment-classification recommendation. The LLM runs the conversation, while deterministic code owns every rule with legal weight and validates every reply before it ships. Every conversation traces back to the exact prompt version behind it, and a simulated-user evaluation harness means prompt changes ship with measured evidence, not a hunch.

Azure OpenAI · FastAPI · PostgreSQL · Server-Sent Events · OpenTelemetry · Application Insights

3–5questions to an indicative recommendation

LDX (US commercial real estate)

LLM evaluation framework & QA workbench

LDX extracts structured terms from commercial leases with LLMs. Built the QA loop that catches wrong extractions: a YAML-driven evaluation framework with seven validation strategies, plus a workbench that reconstructs the exact prompt behind any extraction, turns it into a test case in one click, and runs the suite from the browser.

Python · Pydantic · pytest · Streamlit · RapidFuzz · AWS S3

92+ test cases across 19 lease-term categories

UK consumer-credit FinTech

Hardening a Databricks lakehouse on AWS

Joined a live lakehouse running 60+ production jobs and 30+ DLT pipelines and owned its next stage: least-privilege IAM per persona derived from CloudTrail evidence and rolled out canary-first, secretless CI/CD with GitHub OIDC, a shared Python package on every cluster, and new finance reconciliation pipelines.

Databricks · Unity Catalog · DLT · AWS · Terraform · GitHub Actions

Secretless CI/CD across AWS and Databricks

nGAGE Talent (UK recruitment)

AI-powered employment classification engine

Built an engine that automates Employer-of-Record classification for nGAGE’s global contractor placements. Decisions come from versioned YAML rulesets pinned to each case, so every past decision stays reproducible and audited, and Azure OpenAI writes only the plain-English rationale, never the classification.

Azure OpenAI · FastAPI · PostgreSQL · Tortoise ORM · Terraform · Bitbucket Pipelines

Live in production, replacing manual legal review

nGAGE Talent (UK recruitment)

AI candidate search across 2M+ documents

Built an AI-powered candidate search API processing over two million CV files across 15 recruitment brands. Hybrid vector, keyword, and semantic ranking, with an event-driven pipeline that ingests new CVs automatically and serves fast, meaning-based search.

Azure OpenAI · Azure AI Search · FastAPI · Container Apps · Event Grid

2M+ documents searchable

What we build

Production AI & RAG

RAG pipelines, hybrid vector and semantic search, AI agents, LLM evaluation and QA infrastructure. AI that’s measured, governed, and reliable - not just impressive in a demo.

APIs & Cloud Architecture

High-performance APIs, event-driven systems, and cloud-native architecture on AWS, Azure, and GCP. Infrastructure-as-code, CI/CD, observability, and cost control built in from the start.

Data Platforms

End-to-end data platforms and lakehouses - ingestion, transformation, governance, and BI. Databricks, Snowflake, BigQuery, modern data stack. From raw data to analytics-ready, properly governed.

How it works

  1. Intro call

    30 minutes on what you’re building, where it’s stuck, and whether we’re a fit.

  2. Written scope

    Milestones, deliverables, timeline, and price, agreed in writing before work starts.

  3. Build with weekly demos

    Working software at every milestone, shown rather than reported.

  4. Handover

    Code, docs, and runbooks in your repositories, so your team can run it without us.

Senior-led, staffed to fit

Every engagement is designed and led by Dmytro Ostapchuk. The person you talk to is the person who makes the architecture calls, writes the code, and is accountable for the system.

When a project needs more hands - front-end, design, DevOps - we bring in senior specialists we’ve worked with before. What doesn’t change: no account managers, no junior hand-offs, one person accountable.

AI can write the code. Someone still has to be accountable for the system.

“Dmytro is a consummate professional. He is easy to collaborate with, receptive to ideas, and exceptionally quick in his responses. His experience shows in every interaction, and the solutions he delivers are consistently robust and perfectly aligned to the requirements. His quality of work is truly unparalleled.”

- Ronnie G., Group Digital Director, nGAGE Talent

“He is an exceptional developer - a genuine talent - capable of planning and building complex AI based applications from the ground up. He requires minimal guidance, quickly understands concepts and requirements and delivers solutions that exceed expectations.”

- James Squires, Founder, UK SaaS company

“Dima delivered some great features for our data extraction pipeline team... This allows our team to QA our extraction accuracy and integrate our findings seamlessly into our testing and development workflows. Dima is a pleasure to work with - easy to talk to and detail oriented.”

- Travis Sanders, Co-Founder & Head of Technology, LDX
Dmytro Ostapchuk

Hi, I’m Dmytro Ostapchuk.

I lead Insight Origin, an AI product engineering studio. I’ve spent 12+ years building production systems for startups and scale-ups across the US, UK, and Europe, and my focus is the gap between a prototype and a production system - closing that gap is the work I care about and the work I’m best at.

Before going independent, I was a Technical Product Manager at DraftKings, where I owned DevOps strategy for two product teams building high-traffic distributed systems. At Hypatos, an AI document-processing company, I led infrastructure and platform strategy. At Sift, I built developer platforms and tooling. I started as a software engineer at DataRobot, building an internal automation platform from zero to company-wide adoption.

That path shaped how I work. I’m an engineer with a product manager’s instincts - I don’t just build what’s asked, I make sure it solves the real problem.

Comfortable with EU, UK, and US East Coast hours · Previously: DraftKings · DataRobot · Sift · Hypatos

Let’s talk about your project.

The fastest way to start is a 30-minute call. Tell us what you’re building and where it’s stuck. If it’s a fit, you’ll get a written scope. If it’s not, you’ll hear that too, with a pointer to someone better placed to help.

Book a 30-minute call →

dima@insightorigin.ai

LinkedIn →

I read every message and reply personally, usually within one business day.