Engineer at the
speed of AI.
Not the speed of legacy.
Legacy systems consume 60–80% of IT budgets — leaving almost nothing for AI innovation. Payoda's engineering teams modernize the legacy, ready the data, and ship AI-first products with senior engineers and AI copilots working in pairs. Three practices. One AI-led delivery model. No lift-and-shift theater.
The Challenge
Legacy systems drain 60–80% of IT budgets. AI ambitions get what's left.
Your roadmap is full of AI products that should ship, data platforms that should support them, and architectures that should have been modernized years ago. Teams are stretched across maintenance, half-finished migrations, and AI experiments that never made it to production. The gap between strategy and execution is where most enterprises lose to AI-native competitors.
Technical debt blocks the AI roadmap
Every quarter you delay modernization, the cost compounds. Monolithic codebases can't host modern AI integrations. APIs that don't exist can't be agentic.
Senior engineers are stuck on maintenance
You can't hire AI engineers fast enough. The ones you have spend 70% of their time on legacy maintenance — not on the AI features your roadmap promised.
Data isn't AI-ready
RAG fails on messy data lakes. Models hallucinate when grounded in stale records. AI demos look brilliant in pilots and break the moment they hit production data.
AI without architecture is theater
Everyone wants AI features. But layering LLMs onto systems that can't handle data, latency, or scale just creates expensive experiments that never ship.
QA can't keep up with AI velocity
Manual testing was already a bottleneck. Add AI-generated code at 3× engineer velocity and your test pyramid collapses. Bugs reach production at the same speed AI ships features.
Strategy decks ≠ shipped products
Consulting firms hand you a beautiful AI roadmap and leave. Staff-aug partners show up with bodies, not opinions. The gap is where AI initiatives go to die.
faster delivery with AI copilots in the loop
engineer productivity — AI handles the boilerplate
AI-augmented engineers across 7 industries
years of engineering. AI-first since 2023.
AI-Led Engineering Services
Three practices. One AI-first partner.
Delivered by Payoda's engineering teams with senior engineers, AI copilots, and shipped-software experience. Not staff-aug bodies. Not slide-deck consultants. Engineering teams that ship — with AI in every layer.
Legacy Modernization, powered by AI.
For CTOs, Enterprise Architects & IT Directors
The systems running your business shouldn't hold it back from AI. We use generative AI to reverse-engineer legacy code, extract embedded business rules, and re-platform mainframes, monoliths, and end-of-life applications onto cloud-native, AI-ready architectures. Strangler-fig patterns for zero-downtime migration. AI copilots accelerating every refactor. No lift-and-shift theater.
- ✓AI-assisted code discovery — LLMs parse COBOL, RPG, PL/SQL automatically
- ✓AI-accelerated translation: legacy → Java, .NET, Python, TypeScript
- ✓AI-driven test generation locks behaviour before refactoring begins
- ✓Cloud re-platforming on AWS, Azure, GCP — AI guardrails from day one
- ✓Codebases handed over Copilot-ready — multiplier from day one
Data Modernization for the AI era.
For Chief Data Officers, VPs of Data & AI Platform Leaders
Generative AI is only as good as the data it stands on. We rebuild your data foundation — warehouses, lakes, lakehouses, streaming pipelines, and feature stores — into an AI-ready platform that powers RAG, agentic AI, and real-time decisioning at enterprise scale. With responsible-AI governance, evaluation harnesses, and lineage baked into every layer.
- ✓AI-ready lakehouse on Databricks, Snowflake, or Microsoft Fabric
- ✓AI-augmented pipelines — dbt, Airflow, Spark with copilots in the loop
- ✓AI-powered data quality — ML drift detection, auto data contracts
- ✓Vector databases & RAG infra (Pinecone, Qdrant, pgvector, Weaviate)
- ✓Agentic AI with LangGraph — tool use, HITL gates, audit trails
- ✓Responsible-AI governance — lineage, evals, model cards, policy-as-code
Product Engineering, AI-first by default.
For CTOs, VPs of Engineering & Product Leaders
We build software products with AI inside — and AI alongside. Generative AI features baked into the product. AI copilots accelerating every engineer on the squad. Agentic workflows running where humans used to click. From discovery to production, AI is in every layer of the SDLC. With evals, guardrails, and cost controls — so AI ships safely and stays audited.
- ✓AI-embedded product strategy — feasibility, ROI, GenAI use-case scoping
- ✓AI-augmented design — Figma + AI for UX research and copy
- ✓Copilot-native squads — React, Next.js, Node, Python, Go on GitHub Copilot + Cursor
- ✓GenAI features done right — LLM search, copilots, agents, multimodal
- ✓AI-driven QA — LLM exploratory testing, self-healing test suites
- ✓AIOps observability — Datadog, OpenTelemetry, AI anomaly & RCA
Why Payoda
Engineering teams that ship. With AI in every layer.
Three reasons enterprises pick us over staff-aug providers and pure-play consulting firms.
Engineering-first, AI-augmented
Engineers who ship — not consultants who only diagram. Every squad equipped with GitHub Copilot, Cursor, and internal AI tooling.
AI architectural opinions, not buzzwords
We tell you where GenAI moves the needle — and where it doesn't. RAG, agents, copilots, fine-tuning: we have shipped all of them. We will tell you which one fits.
Responsible AI by default
Evals, guardrails, RBAC, audit trails, and HITL gates on every AI feature we ship. Lineage, model cards, and policy-as-code in every data platform.
From legacy to AI-native in one engagement
Modernization, data, and product engineering delivered by the same partner — so the AI-ready architecture you design actually gets built.
Engineer faster. With AI inside the team.
Tell us what you are modernizing, what data is in the way, or what AI product you want to build. We will scope it and put a senior team on it.