Request a POC
Datacolor Meridian
Frequently asked questions
How Meridian and Datacolor GSR (General Symbolic Reasoning) cut token cost, lift accuracy, and make enterprise Al predictable, and how we prove it.
What is Datacolor Meridian?
Meridian is an Al FinOps solution that instruments, measures, visualizes, and optimizes enterprise Al spend. Every Al call routes through the solution, giving finance and engineering one shared view of where Al budget goes, and how to reduce it. Its optimization differentiator is Datacolor GSR.
Datacolor Meridian enables Instrumentation (capture and tag every call), Measurement (FinOps metrics and attribution), Visualization (executive, finance, and developer dashboards), and Optimization (routing, caching, right-sizing, and Datacolor GSR). Continuous monitoring and governance wrap all four capabilities.
Datacolor GSR (General Symbolic Reasoning) converts an ambiguous natural-language request into a structured reasoning graph before the LLM starts solving it. Deterministic engines (SQL, search, business rules, Python, APIs) execute each step, and the LLM becomes a planner and explainer rather than the primary computational engine. The result: lower token consumption, lower latency, more predictable behavior, and greater accuracy on long, multistep tasks.
Chain-of-Thought relies on the model to decide how much reasoning is needed, so it reasons token-by-token every time. GSR evaluates the task first, builds a structured execution plan, and passes an optimized request to the model. Because reasoning patterns are reusable symbolic programs rather than generated from scratch per request, GSR avoids large amounts of redundant inference.
Rather than letting the model sequence tools dynamically, GSR establishes dependencies, execution order, branching logic, and recovery paths before execution begins. It continuously verifies intermediate results against contracts and constraints, re-planning or retrying a single step instead of rerunning the whole workflow. That means fewer retries and more predictable, auditable agent behavior.
Savings accrue from two places: cutting unnecessary reasoning on simple tasks, and cutting retries, re-prompts, and execution failures on complex ones. By shifting work from expensive token-based reasoning to deterministic computation, GSR can significantly reduce token consumption and overall inference cost.
Yes. Beyond foundation-model baselines (BFCL v3, TauBench, SWE-Bench Lite, MAVEN), GSR is measured against strong structured-prompting approaches such as ReAct, GEPA, and RLM, as well as leading open and proprietary models.
By comparing identical workloads before and after GSR is enabled - same prompts, models, tools, and test harness - measuring accuracy, latency, token consumption, and total cost. You receive a baseline scorecard, a post-evaluation scorecard, projected monthly and annual savings, and reproducible benchmark artifacts your engineering and finance teams can independently validate.
Minimally. Meridian exposes a drop-in endpoint that fronts any provider behind it, including Anthropic, OpenAI, Azure, and others, so instrumenting a call is largely a matter of repointing it at Meridian. You can start with a small subset of agents or nodes to de-risk rollout, then expand as confidence grows.
A Proof of Concept can be completed in around 2 weeks, with a typical production implementation cycle of 4-6 weeks per client. It's faster when we start from your existing production LLM logs (brownfield) or a clear process document (greenfield). You finish with an endpoint that acts as a drop-in replacement for your current model calls.
Ready to see it in action?
Request a proof of concept, or reach out at with any questions.
Request a POC