AI projects fail on brittle pipelines and data that was never built for the systems meant to use it. We engineer the AI-ready foundation underneath, so your data holds up in production, and your roadmap has a foundation it can depend on.
Our architects design the data architecture and strategy before any build. The foundation fits your requirements and your budget.
Our team creates modern pipelines and storage that reliably move and unify your data, batch or real-time.
Our engineers build and maintain pipelines with agentic tooling and Claude Code, with senior review on everything that ships.
Quality checks, lineage, and access control keep your data dependable and auditable once it is in production.
A foundation only pays off once decisions run on it. Our data scientists build the analytics layer on top of the same foundation this team engineered.
Modernization moves you off constraining legacy pipelines and warehouses onto a foundation that scales with you.
A readiness assessment and a use-case shortlist ranked by value and feasibility. You understand what is worth building now and what your data has to fix first.
A costed, phased plan, with the architecture designed. You take it into budget decisions, with a build-or-buy call made for each component.
Pipelines, ingestion, transformation, and your data lake or warehouse, built and wired to your sources. Our ML knowledge engineers structure PDFs and contracts into queryable form alongside your tables. You get data that moves reliably across batch and real-time, ready for the AI and ML workloads on your roadmap.
Lineage, access control, observability, and data lifecycle management ship inside the foundation, with AI-powered testing validating each pipeline continuously. You end up with governed, auditable data your security team can sign off on, mapped to SOC 2, GDPR, and HIPAA.
A foundation validated against the step-one criteria, handed over with documentation, tests, and monitoring. You run it yourself, or keep Relevant on to extend it, with the people who built it staying with it.
99% of engagements ship on time and on budget. The Relevant AI Delivery Framework™ catches risk early and locks scope at the start. The timeline you approve is the one you get from kickoff through launch.
Up to 50% faster delivery with AI in the loop and bugs reduced by up to 20%. AI carries the repetitive tasks while senior engineers make the decisions. A quicker path to production never costs you reliability.
92% senior-level engineering talent. The data scientists and engineers designing your pipelines have built complex data systems before. They know exactly where AI accelerates the work and where experience has to lead.
ISO 27001:2022 certified and HIPAA-ready, GDPR covered, with clear IP and data-handling rules for every AI tool in the workflow. The foundation your security team can audit and approve goes live.
13+ years building data systems for SaaS, fintech, healthtech, real estate, and beyond, including regulated and high-traffic environments where the data cannot afford to break.
The savings come from where AI carries the load and where senior engineers stay in control. AI generates pipeline code, transformations, tests, and documentation, which is what lets our engineers deliver up to 50% faster and reduce bugs by up to 20%.
Fewer bugs and faster cycles mean a shorter path to production and less spend on rework, while a senior engineer reviewing what ships keeps the speed from costing you reliability. You get the cost and time advantage of AI in the workflow with the accountability of people who have run these systems under real load.
It is better to start with the AI maturity assessment. Most engagements start by auditing your data against the use cases you actually care about, keeping the scope tied to business value rather than a generic cleanup.
Data audit covers:
You walk away with a readiness assessment and a prioritized shortlist that names what is ready to build on now, what is blocking you, and what the foundation needs first.
A senior engineer owns the code. AI accelerates the work by generating transformations, tests, and documentation, but a senior engineer reviews and approves what reaches production, so nothing ships as a black box. Reliability then holds in production through automated data-quality checks and continuously running pipeline observability, which flag anomalies and schema drift before they reach your reports or models.
You get the delivery speed of AI-assisted development with the accountability of people who have run these systems under real load, and a clear audit trail of what each pipeline does and why.
You do, on our recommendation. We assess your requirements, existing stack, and team skills, then recommend the warehouse, orchestration, and processing tools that fit, whether that is Snowflake, Databricks, BigQuery, or another stack.
The choice is made on the merits of your case, not on what we would prefer to work with, and we explain the trade-offs so the decision is one you can stand behind.
You own all of it, in full. We agree on IP and data-handling terms before any work starts, and every pipeline, transformation, and piece of documentation we produce is yours to keep, run, and change.
Nothing in the work locks you to Relevant: we build on standard tools, document everything, and hand over tests and monitoring so your team or any other vendor can maintain it. The point of the engagement is a foundation you own outright, not one you have to keep paying us to operate.
The assessment starts within a week of our first call. Most clients see a first working pipeline or warehouse component within 4 to 6 weeks, depending on how ready your sources and existing infrastructure are.
We deliver in stages rather than in one release at the end, so a usable piece of the foundation lands early and you build on it while the rest is still in progress. Full-scale migrations across multiple legacy systems take longer, but the staged approach means you’re never waiting on the entire build to see value.
With AI in the loop, our engineers deliver up to 50% faster and cut discovery time by half, which is what pulls that first working component forward. The assessment gives you real dates for each phase before any build is funded, so the timeline is one you can plan a roadmap around.
Whichever fits the shape of the work.
With 92% senior-level talent, the engineers you get in any model work with minimal oversight and set direction rather than wait for tickets.
None beyond the readiness assessment. We’d rather show you what your data can support and what it needs first, before you commit to a larger build.
Many clients start with one pipeline or one source, see it run reliably in their own environment, then expand from there once it’s proven. That keeps your initial outlay small and tied to something you can evaluate, rather than a long contract signed on a promise. Everything we build is yours to keep and run, so even a single-pipeline start leaves you with a working asset you own outright.
Yes, and we build for that from the start. The foundation ships with documentation and tests that stay current, observability that your team can read, and pipelines designed to be maintained rather than babysat.
Many clients take it in-house and run it from there. Others keep Relevant on to extend it, and with 96% retention, the people who built it are the ones who stay with it. Nothing in the work locks you to us.
96% retention. The engineers who build your foundation are the ones who stay with it. You sidestep re-onboarding, lost context, and the gaps that open up when teams churn mid-build.
A 4.9 Clutch rating from clients who point to transparency and delivery quality in their own words.
Readiness is the thing to settle before the build. Bring us the use case, and you’ll leave knowing what your foundation needs and what it would take to get there.
Tech label
Tech label
Healthcare
