Each step of your LLM workflow can run in a known order, with a check before the next one starts, so a wrong result stops at the step that produced it. Relevant builds workflow logic you can read and test.
Relevant engineers turn a business process into an LLM workflow of prompt chains, tool calls, and decisions in a set order, and hand any step that searches your documents to RAG development.
Framework choice follows the workflow: LangGraph for long-running flows with branches and saved state, LangChain for standard chains, and plain code where a few API calls do the job.
Every tool the model may call has a strict input and output schema: our engineers write each one against your ERP, CRM, or internal API and reuse connectors from AI integration services where they exist.
A classification step and a drafting step rarely need the same model, so Relevant assigns each step its own and connects every call to your company routing rules through our AI orchestration development services.
A test set for every step shows how a prompt, tool, or model change affects results, and our engineers run it before any change reaches production.
Our architects score your candidate processes on case volume, how clearly the rules are written, and the time each case takes today. You choose the first process from the ranked list.
Relevant writes the design for the chosen process: each step, the tool it calls, the model it uses, and the steps where an agent decides the path. The design also sets the pass criteria for every step, and the same document becomes the build scope.
Engineers build the workflow and run it on live cases next to your current process. Your staff handle every case as usual while the workflow produces its own results in the background.
One report compares both sets of results case by case: accuracy, handling time, and the cases the workflow passed to a person. Where a step needs more accuracy than a general model gives, Relevant adds LLM development.
One case type moves to the workflow first, such as standard invoices. Relevant adds the next type once the results hold, and each expansion goes through the same comparison.
Prompt and model changes ship under LLMOps services on a release schedule you set, and your team owns the code, prompts, and tests.
5 Claude Certified Architects. Available to join your project now. Architects with the Claude certification design your workflow steps, tool schemas, and prompts.
Up to 50% faster delivery and up to 20% fewer bugs with AI-assisted development. AI assists with code and test cases, and senior engineers make every design call.
ISO 27001:2022 certified, HIPAA-ready, GDPR covered. Each workflow handles customer and patient records under the same controls Relevant applies to its own delivery.
98% client satisfaction. Customers trust Relevant with new projects, and the first workflow sets the pattern for the ones after it.
10+ years of data engineering expertise. Relevant engineers prepare the records each workflow reads, from source extraction to field-level cleanup, so every step starts from consistent data.
Custom software only. A finished workflow runs on your own accounts, so its running cost comes down to model usage and your hosting.
Yes, the prototype usually becomes the starting point. Three things tend to need work first. Prompts often sit inside framework defaults, where nobody can see or version them, so our engineers move each prompt into the code next to its step. Prototypes rarely have tests, so step tests come next, built from cases the prototype already handled. Early builds often run on framework versions that trail current releases, so Relevant pins a version and replaces only the parts that block production. A full rewrite makes sense when the prototype packs several steps into one prompt and no step can be tested on its own.
Build cost follows the shape of the process, and you can count each factor before a first call:
Running cost follows the number of model calls per case, the size of each call, and your monthly case volume. The design picks each step’s model with that volume in view. The workflow design and plan arrive on average 14 days after kickoff. To size a specific process, contact us with its step count and monthly volume.
The bar comes from your current process. Pull a sample of recent cases and have a second person recheck each one. The share of cases they correct is your current error rate, and the workflow’s pass criteria start from that figure, step by step. If your team has no time for the recheck, Relevant runs it on your records during the first stage, with nothing prepared on your side. Regulated steps, such as a sanctions check, often carry a stricter bar than the process average, and the design records that bar separately.
A rules engine fits when every decision in the process can be written as an if-then rule and the inputs arrive as structured fields. A rules engine costs less to run, and its output stays exactly as written. An LLM belongs at steps that read free text: an email, a scanned contract, a claim description. Many processes mix both, and Relevant builds them that way, with rule steps in plain code and model calls only where text needs reading. If a process has no free-text step at all, the design says so and recommends the rules engine.
Each step checks its output against the criteria set in the design. A case that fails a check leaves the workflow and goes to a review queue, with the completed steps and the reason it stopped. A person finishes the case from there. The queue also shows which case types stop most often. Once a type appears often enough to justify the work, Relevant adds a branch for it and runs the branch through the same comparison as the launch.
Workflow logic you can read and test shows them the path, step by step.
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