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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 the Enterprise AI Control Plane — one gateway that instruments, measures, visualizes and continuously governs quality, performance and cost across every agent, MCP server and model in your estate. Its optimization differentiators are Eval-Based Routing and Datacolor GSR.
Datacolor Meridian enables Instrumentation (every call captured and tagged, automatically), Measurement (quality, latency and cost, down to a single call), Visualization (one dataset, a dashboard per audience), and Optimization (continuous eval-based routing and Datacolor GSR, applied only where they pay off). Governance — PII detection, content policy, prompt-injection safeguards, RBAC and full audit trail — is enforced on every call before it's routed.
Continuous eval-based routing sends each task to the model that clears your quality bar at the lowest cost — not a static rule set, but a routing decision that re-learns as models and prompts change. You pick the dial: quality up with cost held in check, or cost down with quality held constant. It runs continuously, so a model update or a new provider doesn't require re-tuning the routing logic by hand.
Datacolor GSR (General Symbolic Reasoning) converts an ambiguous natural-language request into a structured reasoning graph before the LLM starts solving it. Deterministic engines execute each step, and the LLM becomes a planner and explainer rather than the primary computational engine — unlike Chain-of-Thought, which relies on the model to reason token-by-token every time. GSR evaluates the task first and passes an optimized, reusable execution plan to the model, avoiding large amounts of redundant inference.
They're independent engines you can turn on separately. Continuous eval-based routing picks the right model for each task on quality and cost; Datacolor GSR goes further, producing frontier-grade output from a smaller model by planning before it runs.
Savings accrue from three places: routing each task to the model that clears your quality bar at the lowest cost, cutting unnecessary reasoning on simple tasks, and cutting retries, re-prompts and execution failures on complex ones. Quality improvements come from the same mechanism — eval-based routing only optimizes cost once a task clears your quality bar, and GSR’s structured, verified reasoning plan produces fewer errors than an unverified, ad hoc reasoning chain, especially on long, multi-step tasks.
Yes. MCP and A2A support is built into the same gateway that routes your LLM calls, so tool calls to MCP servers are routed, budgeted and audited the same way — one control point for the whole estate, not a separate integration.
By comparing identical workloads before and after optimization 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 — OpenAI, Anthropic, Google, Azure, AWS Bedrock and 100+ others, scoped to an approved-model allowlist — so instrumenting a call is largely a matter of repointing it at Meridian. MCP and A2A traffic are supported the same way. 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.
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