Traffical for AI engineering teams

Optimize AI features against real user outcomes

Offline evals tell you whether an output looks good. Traffical tells you whether an AI change improves task completion, conversion, and cost with real users — for which segments, and whether it should roll out.

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Complementary to your eval and observability stack — production outcomes, not offline scores.

assistant.model MIGRATION CANARY

control · 80%

model-large-4.2

task completion 84.1%

cost / session $0.048

treatment · 20%

model-mini-5.1

task completion 83.8%

cost / session $0.029

task_completion_rate (guardrail) −0.3% · within bounds

inference_cost_per_session −38% · 99% prob.

Agent proposes: ramp treatment to 50% — completion guardrail holding, cost saving confirmed

Measured on real sessions, in your warehouse — not on a benchmark set

YOUR EVAL STACK ANSWERS

"Does this output look good?"

Offline scores, judge metrics, regression suites. Necessary — and not sufficient for a rollout decision.

TRAFFICAL ANSWERS

"Does this change improve the product — and should it ship?"

Task completion, conversion, retention, and cost with real users — per segment, from your warehouse, with a governed rollout decision at the end.

Every decision in your AI feature, measurable

Models, prompts, tools, retrieval, workflows — all typed parameters with a lifecycle, not config scattered across code and consoles.

assistant.model

Prompt & model selection

Compare prompts and models on task completion and conversion with real users — not just offline scores.

agent.tools

Tool availability

Which tools an agent may call is a typed parameter — enable, restrict, and measure the effect per surface.

rag.retrievalDepth

Retrieval & fallback settings

Chunk counts, rerankers, and fallback chains tuned against answer quality and latency in production.

agent.workflowVariant

Agent workflow variants

Plan-then-act vs. single-pass vs. tool-heavy — release workflow changes through canary and experiment.

ai.maxCostPerTask

Cost vs. completion trade-offs

Set cost ceilings as parameters and measure exactly what each dollar of inference buys in outcomes.

assistant.persona

Personalization with bandits

Contextual bandits pick the best configuration per segment when one global winner is the wrong answer.

assistant.modelVersion

Safe model migrations

Canary provider or version swaps with auto-revert — completion guardrails decide, not hope.

ai.temperature

Generation settings

Temperature, token limits, and sampling as governed parameters — changed without a deploy, measured always.

ai.fallbackChain

Degradation behavior

What happens when the primary model times out is a measured decision, not an untested code path.

Beyond the A/B test

When there is no universal winner, let allocation adapt

Some AI decisions shouldn't converge on a single value. Contextual bandits shift traffic toward what works per segment — power users get the deep workflow, quick tasks get the fast model — under the same guardrails and governance.

agent.workflowVariant EXPERIMENT · EVEN SPLIT

plan-then-act 33% · reward 0.74

single-pass 33% · reward 0.68

tool-heavy 34% · reward 0.55

Static experiment: equal allocation while evidence accumulates. Toggle to see the bandit take over.

Safe model migrations

Every provider or version change is canaried with auto-revert on guardrail breach — never a big-bang swap.

Governed like everything else

AI parameters follow the same risk policies, approvals, and decision logs as pricing or ranking changes.

Cost is a first-class metric

Inference cost per session sits next to task completion in every readout — trade-offs are explicit, not discovered on the invoice.

Design partnership · 2026

Ship your next model migration on evidence

An 8-week pilot on one AI surface: parameterize it with your coding agents, canary it, and read the outcome in your own warehouse metrics.

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