When analytics arrives late, or no one trusts it, your team falls back on instinct. We build AI-powered analytics that forecast outcomes, surface what matters, and reach the decision-maker in time, so the call rests on data your teams can trust.
Getting value from data takes two jobs: building it and drawing insight from it. We do both, so the team running your analytics is the team that handled the AI-powered data engineering before.
Our architects define the metrics, KPIs, and roadmap your analytics runs on, so the work ties to decisions you actually make.
Semantic modeling done once: taxonomies and metric definitions read the same way by every team and every model.
AI surfaces the patterns and answers your teams would otherwise wait on an analyst to find.
Data scientists build models that forecast outcomes and recommend the action each result calls for.
Model output goes where the decisions are made, inside your products and operations, rather than a separate tool.
Beyond surfacing the insight, agents take the next step inside the boundaries your team sets, built on the agentic systems our engineers design and deploy.
Our engineers build custom models for the problems that off-the-shelf analytics cannot answer.
A working session frames the decisions worth improving and the question each one really asks of your data.
An assessment checks whether your data is complete, reliable, and granular enough to model. You learn what holds up, what needs fixing first, and who owns each source, before any model gets built on it.
Forecasting, scoring, or prescriptive models are built and tuned on your own data, with AI in the loop and senior review on what ships. AI-powered testing measures each model against held-out results.
The validated output reaches the decision, as a dashboard, an alert, or embedded intelligence, with enablement that gets your people trusting 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 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, with SOC 2 and GDPR built in from day one. Explainability and audit trails come standard in every model we ship, so the work stands up to EU AI Act review.
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.
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.
Standard BI describes what has already happened, through dashboards and reports on past performance. AI-powered analytics adds three things on top:
We also build the analytics faster, with AI in the loop and senior data scientists reviewing what ships. The practical difference is analytics that tells you what to do next, delivered where you make the decision, rather than a report you read after the moment to act has passed.
Yes, we can. A large share of the answers executives need sit in contracts, reports, and PDFs rather than in tables, and analytics that ignores them answers only half the question. We structure that content into governed, queryable form alongside your databases, so forecasts and dashboards draw on both.
Where the document volume calls for a dedicated ingestion build, our team lays that foundation first.
We start by checking. The first step assesses whether the data behind each decision is reliable and granular enough to model, and often it is, at least for your highest-value questions. Where the data holds up, we build the analytics directly. Where a forecast depends on data that is not ready, we name that gap, and our AI-powered data engineering team builds the foundation first, rather than putting models on numbers that will not hold. You get an honest read on what your data can answer now and what has to be fixed before it can answer more.
Against the decisions set at the start. The first step defines the outcome each piece of analytics is meant to improve, which becomes the benchmark we hold the work to: a sharper forecast, a faster call, a measurable lift in the metric you named. Model accuracy is validated against held-out data before release, and monitoring tracks it in production so a model that was accurate at launch does not quietly degrade. You get analytics tied to business outcomes you can point to.
We agree on IP and data-handling terms before any work starts, operate within controlled boundaries, and design governance and access control into the analytics itself. We are ISO 27001:2022 certified and HIPAA-ready, and SOC 2 and GDPR constraints shape the approach from discovery, so what goes live is something your security and compliance teams can review and approve.
Where development touches sensitive data, we work with synthetic or anonymized data, and we do not use your proprietary data to train third-party models
Every model, the code behind it, the dashboards, and the documentation are yours to keep, run, and change, and we agree IP and data-handling terms before any work starts so ownership is settled up front.
Your team can run the analytics in-house from day one, with monitoring and documentation built to be read and maintained by your own people, or keep us on to refine models and add new ones as fresh decisions surface. We hand over everything needed to operate the work, so nothing is structured in a way that locks you to Relevant. The point of the engagement is a capability you own outright, not a dependency you keep paying to maintain.
Discovery starts within a week of our first call. Most clients see a working first model or dashboard within 4 to 8 weeks, depending on how ready the underlying data is, and we deliver in stages rather than holding everything back for one release at the end.
The first step often surfaces a quick win, a decision your data can already answer, so a usable result lands early while larger models are still in development. Complex, multi-source builds take longer, but the staged approach means you’re acting on real output before the full scope ships.
Yes, and for some clients that is the whole engagement. Where a roadmap needs skills that take months to hire, ML knowledge engineering and senior model work among them, our people embed directly into your function and carry the work with your own analysts.
Other clients keep the work separate: a scoped build for a single decision, or a dedicated team delivering the roadmap end to end. The three models coexist, and plenty of engagements move between them as the work matures.
Yes, and we build for that from the start. Models ship with documentation that your team can read, and the enablement to keep them honest as your data changes. Many clients take the analytics in-house and run it from there. Others keep Relevant on to refine models and add new ones as fresh decisions surface, and with 96% retention, the people who built it are the ones who stay with it. Nothing in the work locks you to us.
A 4.9 Clutch rating from clients who point to transparency and delivery quality in their own words.
By the time most analytics lands, the call is already made. Bring us the case, and you’ll leave knowing what your data can answer and how to get it there in time to act.
Tech label
Tech label
Healthcare
