Proven With Frontier Labs
A look at engagements where our diagnosis, data, and training validation left clients with a measurably better model.
Jun 26, 2026
Elite Engineering Networks for Large-Scale Repository Generation
Challenge
A frontier research lab needed to train a code intelligence model to build entire codebases from scratch. Single-file snippets lacked structural context. The target required 10,000 production-grade, multi-file repository datasets with complex configurations and comprehensive test suites.
Solution
Tiki AI deployed a vetted network of elite software engineers using a custom synthesis pipeline to build 10,000 functional repositories from scratch. Every dataset featured dense code reasoning traces, rigorous peer reviews, and fully verified execution paths.
Impact
The high-fidelity repository datasets directly advanced multi-file code synthesis capabilities. The trained model demonstrated a sharp reduction in compilation errors and successfully mastered complex repository-level generation.
"Tiki AI cut months off our pipeline by delivering 10,000+ multi-file repositories that compiled flawlessly out of the box. No boilerplate, no data-cleansing overhead, just pure engineering precision."
May 11, 2026
RLHF Alignment for Complex Agentic Trajectories
Challenge
A frontier research lab needed high-fidelity data to train autonomous systems for multi-step engineering and tool-use tasks. Static examples weren't enough. The pipeline required complex interactive environments and dense agentic trajectories to scale RLHF reward modeling.
Solution
Tiki AI engineered custom interactive environments and deployed technical specialists to execute multi-turn agentic paths. The team captured precise reasoning traces, tool interactions, and error-correction loops, delivering highly structured preference and critique datasets.
Impact
The high-signal trajectory datasets significantly enhanced autonomous planning capabilities. This breakthrough directly drove the safe production deployment of the client's next-generation agentic framework.
"We struggled to find a partner capable of handling multi-turn agentic environments. Tiki AI built the exact custom sandboxes we needed, capturing the dense reasoning traces that finally unlocked our reward modeling."
Mar 9, 2026
Closing the Agentic Tool-Use Gap for a Frontier Foundation Model
Challenge
A frontier AI lab's foundation model fell well short of SOTA in agentic scenarios that require autonomously invoking MCP (Model Context Protocol) tools. Tool-call selection was unreliable, multi-run execution was highly unstable, and the model frequently stalled on long-horizon tasks, looping on self-doubt until it burned through the context window without completing the job.
Solution
Tiki AI diagnosed the model's failure modes through targeted MCP benchmark evaluation, then built a data-production framework with dataset ratios engineered against each weakness. Each of the 1,200+ tasks shipped with a prompt, environment files, an RL-ready verification script, and full execution trajectories for SFT, sourced and validated directly with domain experts.
Impact
The first 100-task batch lifted the model's agentic tool-use capability by 9%. After full delivery, capability improved 22% overall, and the model's rank on a leading agentic tool-use leaderboard climbed from #10 to #2, surpassing several SOTA models in the process.