AI transformation & stack modernization since 2019
We take AI from pilot to production — and modernize the platforms, data, and delivery practices it depends on. Senior engineers who set the strategy and write the code.
Close the gap between
AI ambition and production.
Most AI experiments never make it past the demo, on systems that weren’t built for them. We find the workflows where AI earns its keep, and modernize what sits underneath so it runs reliably at scale.
Then we keep it in production past the first invoice — a YC startup’s provider bill cut 47.7%, W6W taken from 10 to 152 shipped integrations a week.
Senior leadership with every engagement. Code, runbooks, and a team that can run it — all yours.
Find the workflows where AI pays for itself, then build it into production — agents, automation, and the evaluations and guardrails that keep them honest.
Re-architect the legacy systems, data, and infrastructure AI depends on — monoliths into scalable services, manual releases into automated pipelines.
Structured governance assessments and frameworks that prepare your AI systems for enterprise procurement and regulated markets.
Client stories
From the first working agent to the systems behind it. Here’s what changed.
Inside a YC startup's AI cost optimization: model routing, context trimming, and self-hosted inference, with a $230K-to-$120K monthly spend breakdown.
An agent harness that selects, builds, tests and ships API connectors at scale — with the weekly budget acting as admission control rather than a dashboard.
How we built an end-to-end agentic outbound system that automated prospect discovery, deep research, and personalized dossier generation for a fast-growing event management BaaS.
How we evolved from a fixed four-agent pipeline into a dynamically scaling mesh of Claude-powered clusters — with a stakeholder map that knows who to call, when, and why.
Their agents had turned—trapped in infinite loops, feeding on each other, burning 800% more than expected. We hunted the outbreak, contained the infection, and delivered the survival guide.
How we built a multi-agent orchestration system that eliminated context loss and automated development workflows across three client engagements.
Built an enterprise-grade AI agent that understands and acts across event data, integrated apps, and complex workflows with security-first architecture.
Transformed app development from 2-3 weeks per app to 2-3 apps per week through intelligent automation and secure runtime infrastructure.
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The third part of the series: how we package the posture assessment and measurement layer into a complete AI governance framework — and the Stand-Prove-Procure Method we use to build one with every client.
Common questions
ProductiveHub is an AI transformation and modernization consultancy for growing companies. Senior engineers lead the work and write the code, from AI agents to rebuilt legacy platforms. These are the questions buyers ask us first.
It takes AI from an idea or a stalled pilot to software your team uses every day. We find the workflows where AI pays for itself, build the agents and automation, and modernize the systems and data underneath so they hold up in production. After launch, we keep cost, reliability, and governance in check.
Common blockers are unclear ownership, no agreed measure of success, and data or systems that weren’t built for production. We give each initiative one owner, a clear bar for production readiness, and criteria for stopping work that isn’t paying off. Then we measure the outcome it is meant to change: hours saved, cycle time, or cost per task.
AI adoption alone doesn’t guarantee lower costs. In the Federal Reserve’s 2025 survey of US businesses with 1–499 employees, published in 2026, 77% of firms using AI reported no change in labor costs, even as 71% reported higher productivity.
Usually a short one. Before you spend on models, we check three things: whether your data is clean and reachable, which workflows are worth automating, and who will own the result. You leave with a ranked use-case roadmap and a clear starting point.
Yes. Every engagement includes senior technology leadership: a fractional CTO, or a fractional chief AI officer (CAIO) when the work is mostly AI. The person who sets the architecture also writes code, manages your developers, and stays accountable once it’s live.
Yes. Software project rescue starts with an independent code audit: what’s built, what works, and what your team or agency is actually shipping. Then we lead the existing developers to delivery or plan a clean handover, with clear priorities and ownership.
In stages, while the old system keeps running. We move legacy PHP and .NET platforms, monoliths, and aging databases to modern stacks piece by piece, with automated release pipelines to make changes repeatable.
For Cohost, new runtime infrastructure and an AI-powered development pipeline took delivery from 2–3 weeks per app to 2–3 apps a week.
Measure the cost of every task, route it to the lowest-cost model that still passes your quality tests, trim context, cache prompts, and set hard budget caps. For a YC-backed startup, model routing, context trimming, and self-hosted inference cut AI provider spending by 47.7%.
If enterprise buyers are sending you AI security questionnaires, it’s time to put governance in place. We start with an AI posture assessment, then build the controls and evidence procurement teams and auditors ask about: an AI use policy, a risk register, vendor reviews, and ongoing monitoring.
