An autonomous AI researcher for your whole AI estate
ModelShift finds every AI system across your repositories, decides on its own what to measure, runs controlled experiments on your data and hands you improvements with the evidence attached. You answer a few questions. It does the research.
The AI cost graph
ModelShift connects your code, models, traffic and bills into one graph. It finds the combinations that burn money - a frontier model inside an agent loop with no cache - ranks them by dollars, and hands you the fix.
Why teams switch
Six questions, answered continuously
Every model call, agent, tool, prompt, RAG pipeline and provider, discovered from your code and traces.
A living architecture graph with source locations, dependencies and change impact.
Quality, reliability, latency and cost per system - measured against explicit baselines.
Opportunities ranked by a transparent score: value, confidence, cost, risk and effort.
Structured hypotheses and budget-capped experiments on your own data, with statistical rigor.
Evidence-backed recommendations, reviewed pull requests and savings verified in production.
How it works
Pick GitHub, Azure DevOps, GitLab or Bitbucket, paste a read-only token and select the repositories. Traces and usage APIs are optional.
It maps every AI system, chooses KPIs, forms hypotheses and plans experiments - asking you only what it cannot decide. Budgets and approval gates are never exceeded.
Winning changes arrive as reviewed pull requests with before/after results. Savings are counted only once verified in production.
Platform
Static analysis of your repositories - never executing your code - maps models, agents, tools, prompts, retrieval and routing into one graph. Every finding cites the file and line, with a confidence level you confirm or correct.
OpenTelemetry, Langfuse, LangSmith or CSV traces with the full span hierarchy: cost, tokens, latency, retries and errors per model, application and agent - and an honest report of what is not yet visible.
KPIs at three levels - business, AI quality and operational - with deterministic, reference, rubric-judge and human methods. LLM judges are calibrated against human labels, never treated as ground truth.
An autonomous research agenda: opportunities from code, traces, evaluations and new model releases become structured hypotheses, prioritized transparently and approved by you.
Staged screening (50-100 examples) before larger validation runs, paired comparisons on identical examples, confidence intervals, fixed seeds and hard budget caps. Small samples are reported as inconclusive.
Model routing, caching, prompt and architecture changes with the exact code patch, evaluated against the baseline and delivered as a draft pull request. Nothing merges or deploys without your approval.
Complete observable agent trajectories: steps, tool calls, loops, retries and handoffs - and what separates successful runs from failed ones.
New and changed models mapped to your use cases, screened only under your provider policy, residency and privacy rules.
Potential, experimentally validated and production-verified savings - documented, separated and never double-counted.
Evidence, not estimates
Found in code and usage patterns. Clearly labelled - never presented as savings.
Measured in a controlled experiment on your data that met the agreed quality criteria.
Measured after the change shipped: real cost against the same traffic at the old unit cost.
Each recommendation includes the problem, the evidence in your code or traces, root cause, proposed change, expected and measured impact, confidence, risks and how it was validated.
Autonomous, but controlled
Security, tenant isolation, permissions and auditability are the foundation, not features added later.
No. Observability is one input. The platform discovers your AI systems, measures them against baselines, designs and runs controlled experiments, and delivers verified improvements - continuously.
No. ModelShift works out-of-band from your repositories, traces and usage APIs. Production latency is unchanged and there is no new point of failure.
Repository code is analyzed statically and discarded; it is never executed or sent to a model provider. Evaluation data goes only to providers your policy allows, after PII masking, within budgets you set.
Each recommendation shows its evidence and its class: estimated, experimentally validated, or production-verified. Experiments report sample size and confidence intervals, and inconclusive results are labelled as such.
No. It covers LLM applications, RAG, pipelines, APIs and agentic systems. Agent trajectory analysis is an advanced capability on top.
Request a demo
We will walk through the platform on a clearly labelled demo workspace, or on one of your repositories if you connect it read-only during the call.