Your agents run on Salesforce, SAP, Microsoft, and your own stack. An orchestration layer you own can coordinate all of them from one place. Relevant builds it with model routing, token budgets per team, and a record of every agent action.
Our architects map every agent, model, and provider you run and design the layer around them, with AI agent development supplying any agent the design still lacks.
Relevant engineers build the routing service that sends each request to the model that fits its task and its data, and keeps sensitive requests on a private LLM deployment inside your environment.
One workflow can span agents from different platforms: Relevant builds the hand-offs between them and passes each agent to AI agent monitoring and orchestration for tracing in production.
Agent pilots that proved their value reach your ERP, CRM, and data platforms with enterprise AI agent deployment, under the rules the layer already applies.
Our architects interview the owner of each AI use case and pull usage and spend data from your provider accounts. The roadmap sets scope, budget, and the order of work. You approve it, and the build starts from there.
Each workload receives a hosting recommendation from our architects, based on data sensitivity, request volume, and cost. You choose which workloads stay in your environment and which go to external providers.
Together we pick the first use case for high token spend and low data risk. Relevant engineers build any model or agent it still needs through our AI software development services. You review the first report on cost per request and answer quality.
Connecting the layer to your ERP, CRM, identity provider, and data warehouse is the scope of our AI integration services. Relevant engineers write each connector and test it at your production volumes. You sign off each connection after its test run.
Relevant moves the remaining use cases onto the layer team by team, in the order your roadmap set. Each team starts with a dashboard for its requests, spend, and response times.
Relevant hands over the source code, routing rules, and runbooks. You choose how long Relevant provides LLMOps services after launch, from model upgrades to cost reviews.
Up to 50% faster delivery, up to 20% fewer bugs. Our AI-assisted development method keeps senior engineers directing every decision while AI assists with code and tests. Every release passes senior code review.
ISO 27001:2022 certified, HIPAA-ready, GDPR covered. Relevant designs access rules and request logs to the standards that fintech, pharma, and healthcare auditors apply.
98% client satisfaction. Relevant delivers software for enterprise clients including Volkswagen and AstraZeneca, and customers trust the team with new projects.
246 projects delivered. The portfolio spans fintech, pharma, healthcare, and energy, where AI systems answer to regulators as well as users.
Custom software only. Every model and provider choice follows your cost and data requirements, because Relevant builds custom software and sells no product of its own.
10+ years of data engineering expertise. Relevant engineers load request logs and spend data into your warehouse, where they sit alongside the rest of your business data for reporting.
Platform agents keep running where they are. Each platform routes its own agents to the vendor’s chosen models, so the layer leaves those internal model calls alone. The layer takes over at the seams between platforms. It handles the hand-off when a Salesforce agent passes a case to a custom agent, the identity check when an agent reads from your data warehouse, and the log that records both steps. Your custom agents and applications route every model call through the layer.
A platform fits when most of your AI runs inside one cloud provider’s ecosystem, a handful of teams use it, and the platform already supports the models you need. Buying costs less in that case, and Relevant tells you so in the first conversation.
A custom layer pays off when three conditions meet:
Some companies sit between the two cases. For them, our engineers build the custom parts around proven open-source components, so you pay for development only where your rules differ from the standard.
Five counts set the size of the work, and you can pull each one before a first call:
If some counts are unknown, the first step of the engagement produces them, and you need nothing prepared beforehand. Relevant delivers the orchestration roadmap on average 14 days after kickoff, with scope, budget, and timeline for the build. To get a figure for your own setup, contact us with the counts you have.
Yes, the layer can take them over. Many agents need only a small change. An agent that calls a model provider through its standard API usually moves behind the layer with a new endpoint and a new key, and its logic stays as written.
The work grows in two places.
For a prototype with neither tests nor documentation, a rewrite on the layer often costs less than a migration.
The layer needs one accountable owner, usually the head of the platform or infrastructure team, because every AI request passes through it. Two other groups set its rules. Security defines access policy: which roles, agents, and types of data may reach which models. Finance sets the budget for each team and signs off changes. Relevant sets up the review cycle during the build: who approves a new model, who changes a budget, and who reads the monthly spend report.
Three numbers show it: cost per request for each use case, the share of requests served by smaller models, and the time a new use case takes from approval to production. You can run a quick check today. Pick your largest AI use case and look for its monthly model cost in your provider invoices. If the invoice shows one total for the whole company, per-use-case cost is the first figure the layer gives you, and reporting starts as soon as your first use case runs through it.
An orchestration layer you own stays in place through every change.
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