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Human-guided AI is how AI works in regulated environments. In compliance, fraud, and audit workflows, speed is not enough. Systems must be explainable, auditable, and defensible. Autonomous-first AI fails where accountability matters: -Hallucinations -Silent drift -Unclear decisions -Weak audit trails “The model said so” does not hold up. The shift is architectural: -> Confidence-based routing -> Deterministic validation -> Human gating before execution -> End-to-end traceability This is partial autonomy: -Routine work scales -Edge cases get expert review -Every decision is reconstructable Governance is not a layer. It is the system.

Turing operates at the intersection of frontier research and enterprise deployment. Our experience with leading AI labs informs what’s realistic, reliable, and ready for production. That perspective helps enterprises move faster, avoid costly missteps, and deploy AI systems that scale within real regulatory and operational constraints. turing.com/blog/frontier-…

How would this work in your enterprise? Talk to us. turing.com/intelligence/c…

CASE STUDY: Better code models need better benchmarks. We partnered with a client to build a dataset that shows where models actually break, not just where they succeed. 200+ SWE-bench style Java tasks 20+ real GitHub repositories Each task includes a validated patch, reproducible tests, and a trainer-authored issue prompt The goal was simple: reflect how bugs are found and fixed in the real world. The problem: Most benchmarks rely on clean, solvable examples. Real pull requests are not like that. They are messy, uneven, and often hard to resolve. Our client needed to understand: -Where their model succeeds -Where it fails -How well it generalizes across real codebases The approach: We curated tasks from high-quality Java repositories with strict criteria: -Reproducible test failures before the patch -Clean passes after the patch -Meaningful logic changes only -Stable compilation throughout Each repo was containerized in Docker to ensure consistent, isolated test execution. When issues were missing, Turing trainers wrote them. Every prompt was: -Problem-focused -Neutral and solution-agnostic -Aligned with test behavior for clean evaluation We also balanced difficulty: -About 30 percent solvable -About 70 percent designed to expose failure modes The outcome: A benchmark that does more than measure accuracy. It reveals capability. Teams can now: -Test model performance on real bugs -Identify breakpoints across complexity and context -Analyze failure patterns with precision This is how you move from optimistic benchmarks to real insight.

Request a sample task featuring a curated issue prompt, validated patch, pass/fail test states & metadata on difficulty, solvability, and repository source: turing.com/case-study/cur…

Important Project Lazarus update: Back in December, @Turingcom pioneered acquiring real-world startup/enterprise codebases and operational data to train frontier AI models. @steph_palazzolo at @theinformation broke the story on day one. Incredible reporting that helped define a new category. The companies might be dead, but the human intelligence that built them can live on, powering the next generation of frontier models. Lazarus from Turing resurrects the spirits of dead companies. Now we're scaling massively. Buying all data assets from active and inactive companies. Founder or investor or operator with data to monetize? Hit me up. DM or email jonsid@turing.com

Case Study: Most AI agent evals are flawed. They measure outputs. Real agents operate across 80–200+ actions, tools, and OS environments where failure is gradual, not binary. At Turing, we built a new evaluation framework: -900+ deterministic tasks -450+ parent–child pairs -1800+ evaluable scenarios via prompt–execution swapping • 6 domains, balanced across Windows, macOS, Linux • 40% open-source, 60% closed-source tools Each task includes full telemetry: -screen recordings -event logs (clicks, keystrokes, scrolls) -timestamped screenshots -structured prompts, subtasks, and metadata The key idea: structured failure. Instead of injecting errors, we create them by swapping execution and intent: -Parent prompt + Child execution -Child prompt + Parent execution This produces controlled, classifiable failures: -Critical mistake -Bad side effect -Instruction misunderstanding With calibrated complexity (80–225 actions) and strict QA, this becomes a fully reproducible benchmark. Result: We can measure not just if agents succeed, but: -where they break -how errors propagate -how robust they are across real environments Agents don’t fail at the answer. They fail in the process. Read more case studies below.

That COBOL system you retired 3 years ago? It's sitting in a repo — unmaintained, unused, but valuable. Have a legacy codebase? Schedule a call below.

Benchmarking RTL Agents with 1,500+ Real-World Verilog Tasks for NVIDIA’s CVDP For NVIDIA’s Comprehensive Verilog Design Problems, Turing built a production-grade dataset to evaluate LLMs on real hardware workflows, not simplified prompts. Most existing RTL benchmarks reported > 60% pass rates because they relied on narrow prompts and constrained tests and did not reflect multi-file repos, deep module hierarchies, debugging loops, or real EDA toolchains. CVDP was designed to change that. Turing delivered 1,500+ simulation-ready RTL problems across 13 categories, including: -Spec-to-RTL mapping -Code completion -Testbench and assertion generation -Bug fixing and tool-invoked debugging Three tiers of complexity: -Single-file copilot tasks with golden solutions -Multi-file agentic tasks requiring tool use -Full Git-style projects with >200k token contexts and simulation execution Every task included a deterministic harness, a simulation-passable reference solution, and metadata for coverage and difficulty tracking. Validation ran through Icarus Verilog and Cadence Xcelium, with manual RTL engineer review & ambiguity filtering. The result was tooling realism and measurable failure diversity. Cycle-accurate simulations surfaced real issues: -FSM transition errors -Signal width mismatches -Cross-module reasoning failures -Semantic violations When frontier models were evaluated: -GPT-4o dropped from 63% on prior benchmarks to 29% -Claude 3.7 Sonnet peaked at 33.56% on non-agentic generation -Agentic settings saw an additional 10 to 20% drop CVDP is now one of the most challenging hardware design benchmarks available, enabling category-level error clustering, root-cause analysis, and rigorous evaluation as models advance. -1,500+ tasks. -13 categories. -Commercial and open-source simulation. This is what hardware-grade LLM evaluation looks like.

Request a benchmark sample featuring a debugging task, expected behavior specification, and signal-accurate pass/fail evaluation: turing.com/case-study/ben…

EnterpriseOps-Gym is taking off! 2K downloads in 3 days, trending #6 dataset + #3 paper of the day! Let's keep going!

Turing is featured in @ServiceNowRSRCH's Enterprise Ops Gym paper. We built the task and evaluation backbone: -1,000 prompts -7 single domain plus 1 hybrid workflow -7 to 30 step planning horizons -Expert reference executions with logged tool calls -Deterministic validation for success and side effect control Enabling structured comparison of enterprise agent performance across domains and complexity tiers. Dataset -> Paper -> Website -> Code. Below.

Dataset: huggingface.co/datasets/Servi…

Paper: arxiv.org/abs/2603.13594 Website: enterpriseops-gym.github.io Code: github.com/ServiceNow/Ent…

"When we made our acceptance speech on stage, we did give a shout out to Ethereum. White Rabbit wouldn't have existed without this technology." - @pplpleasr1

Turing contributed to Enterprise Ops Gym, ServiceNow’s new enterprise agent benchmark. We designed 1,000 prompts across 8 enterprise scenarios spanning HR, CSM, ITSM, Email, Calendar, Drive, Teams, and hybrid cross domain workflows. Tasks range from 7 to 30 steps with stateful system updates, expert reference traces, and deterministic verification scripts to evaluate correctness and policy compliance. A rigorous step forward for long horizon enterprise agent evaluation. Learn more: Paper: arxiv.org/abs/2603.13594 Data Set: huggingface.co/datasets/Servi… Website: enterpriseops-gym.github.io Code: github.com/ServiceNow/Ent…

Paper: arxiv.org/abs/2603.13594

Dataset: huggingface.co/datasets/Servi…