Scale your AI infrastructure with
Enterprise-Grade Alignment.
We build, train, and manage dedicated Engineering-in-the-Loop (EITL) teams for complex code evaluation, RLHF, and Supervised Fine-Tuning. Elite operations, zero gig-workers.
Schedule a Pipeline Strategy CallZero Gig-Workers.
100% Enterprise Engineers.
Anonymous freelancers using standard LLMs to clear task queues is the biggest threat to model training. We bypass the open market entirely by partnering directly with verified software development firms.
Closed-Network Sourcing
We source exclusively from secure, verified technology firms. Our contributors are active developers writing production-level syntax daily, not gig-economy students.
Cognitive Stress Assessment
We do not rely on standard automated coding tests. Candidates must manually deconstruct flawed, multi-hop model outputs and map out explicit Chain-of-Thought corrections under tight time constraints to prove their deep reasoning capabilities.
Live Agentic Validation
To get onboarded, contributors must pass rigorous, live assessments inside Docker containers, proving they can securely navigate terminal file systems, trace CLI logs, grade agentic tool-use, and create rubrics.
Adversarial Reasoning Benchmarking
Our engineers handle everything from complex CLI scripting to full-scale containerized deployments, below is a basic example of how models hallucinate and gives incorrect reasoning contrasted with our team's verified ground-truth.
Test our engineers on your hardest edge cases.
Stop guessing if a vendor can handle your logic complexity. Send us 10 of the most difficult prompts your internal Quality Control team rejected this week. We will run them through our War Room protocol and return them flawlessly aligned at zero cost.
# Ingesting client's failed Quality Control queue
const micro_pilot = await Pipeline.ingest(rejected_tasks);
# Routing to enterprise-employed engineers
micro_pilot.assign_to_node({
required_skills: ['Docker', 'Big-O Refactoring'],
clearance: Verified_Enterprise
});
> Output: 10 Tasks. 100% Quality Control Ready.
Engineering the Next Generation of Data
Purpose-built workflows for modern LLM, Agentic, and Multilingual architectures.
High-Fidelity Code Alignment
Multilingual syntax evaluation, logic tracing, and algorithmic refactoring for code-generation models. We evaluate for efficiency, memory safety, and functional execution.
Agentic CLI & Containerization
Secure, sandboxed evaluation for autonomous coding agents. Our engineers test model-generated shell scripts, infrastructure-as-code, and Docker builds in live terminal environments to verify execution accuracy.
Adversarial Red Teaming
Rigorous vulnerability probing for enterprise deployment. Our engineers design complex adversarial attacks, prompt injections, and jailbreak vectors to stress-test model safeguards and ensure strict policy alignment.
Indic & Hinglish Logic Alignment
Native-level RLHF and syntax evaluation across Hindi (Devanagari) and Hindi-Latin (Hinglish) code-switching contexts. We bridge cross-lingual logic gaps to ensure models maintain high reasoning fidelity.
Direct Preference Optimization
Multi-turn conversational ranking and nuanced feedback loops. Our engineers align foundation models with safe, highly accurate reasoning logic using strict, front-loaded consensus protocols.
Deep-Reasoning & STEM (CoT)
Chain-of-Thought generation for complex logic, advanced mathematics, and scientific datasets. We build the step-by-step logic paths that foundation models need to solve multi-step problems.
Execution over Promises.
We don't sell hours; we sell pipeline throughput. See how our enterprise-partnered engineers have resolved critical reasoning bottlenecks for frontier models.
Adversarial Vulnerability Probing (GPT-5/Claude 4.5 Class)
The Bottleneck
Client required targeted extraction of multimodal logic failures and jailbreak vulnerabilities prior to public deployment.
Our Intervention
Deployed a dedicated Red Team to execute multi-turn prompt injections, mapping specific cognitive failure modes across image-to-text inference.
Operational Yield
- • 5,000+ verified attack vectors
- • 92% successful vulnerability extraction
- • Zero-day logic loops identified & patched
Polyglot Algorithmic Architecture
The Bottleneck
Foundation model struggling with memory-leak hallucinations and suboptimal Big-O time complexities in edge-case programming tasks.
Our Intervention
Partnered enterprise developers authored rigorous unit-test suites and optimized reference implementations across Rust, C++, and Python.
Operational Yield
- • 800 production-grade algorithms
- • 100% automated test suite coverage
- • 95.8% Quality Control team acceptance rate
Agentic Terminal Operations & Sandboxed Execution
The Bottleneck
Autonomous coding agent repeatedly failing to navigate terminal file systems, parse logs, and deploy stable cloud infrastructure.
Our Intervention
Engineers authored multi-step CLI reasoning environments. Evaluators graded agent outputs strictly inside live Docker containers.
Operational Yield
- • 300 isolated container environments
- • Complex DevOps validation matrices
- • Pre-identified the 40% capability threshold
Doctoral-Level Chain-of-Thought (CoT) Synthesis
The Bottleneck
Base model required multi-step logic pathways for advanced physics and computational biology reasoning benchmarks.
Our Intervention
Activated our domain-experts from IITs and engineer ground-truth reasoning trees, enforcing explicit logic mapping over implicit assumptions.
Operational Yield
- • 3,500 highly dense logic pathways
- • Peer-reviewed ground truth verification
- • 92% Quality Control team acceptance on first pass
Continuous Integration for Human Data
We ensure your internal Quality Control team never wastes time rejecting basic syntax errors. We hard-gate production behind our Human Data Managers.
1. Synchronous War Rooms
Every data generation cycle kicks off with a live War Room. Our contributors work synchronously with our Data Managers to identify prompt edge cases, clarify ambiguous guidelines, and establish Chain-of-Thought architectures before scaling.
2. The Mandatory Live Audit
No contributor scales their output until their initial tasks are audited and approved directly by our oversight layer. This ensures that algorithmic errors and logic hallucinations never reach your internal Quality Control team.
3. Rapid Recalibration Loop
When your internal Quality Control team updates a guideline or refines a model preference, we instantly propagate that update across our workforce via live recalibration sessions. A rejected logic path is never repeated.
We didn't start as an AI data startup.
We started as software architects.
Most AI alignment companies are built backwards. They launch a web portal, recruit thousands of anonymous freelancers from open message boards, and hope that sheer volume eventually yields high-fidelity data. We recognized early on that this crowdsourced model was fundamentally broken for frontier models.
RocketTech was already an established technology solutions firm. When enterprise teams began training complex foundation models, they quickly realized that open-market gig workers couldn't handle multi-hop logic or containerized code evaluation. They didn't need "annotators" answering prompts; they needed active developers writing production-level syntax.
So, we built a walled garden. We completely bypassed the gig economy to create a private operations division. Every individual who touches your model's data is a verified, fully-employed software engineer operating within our secure infrastructure. No transient contractors. No open-market noise. Just pure, institutional engineering logic applied directly to your pipeline.
Institutional Backbone
Born from a legacy IT infrastructure company, bringing enterprise-grade security and operational maturity to AI data.
Zero Open-Market
We strictly prohibit the use of public crowdsourcing platforms. Every W-2 engineer is vetted and bound by strict NDAs.
Developer-Centric
You wouldn't let a gig worker push code to your production server. We apply the exact same standard to your model's training data.
High-Fidelity Output
Because we control the environment and the talent, our logic validation rates heavily outperform fragmented remote teams.
The Founding DNA
A triad of enterprise engineering and frontier-AI operations.
15-Year Enterprise Architect
Bringing a decade and a half of deep software engineering experience. Ensuring our evaluation infrastructure, security protocols, and containerized sandboxes meet strict, production-level enterprise standards.
Former Scale AI Manager
Having managed massive human data operations at the edge of the AI frontier, bringing battle-tested frameworks for operational excellence, rigid quality management, and elite contributor oversight.
Alignment Specialist
Bridging the gap between raw data operations and enterprise ML objectives. Ensuring that every verified engineer on our roster is perfectly calibrated to output deterministic, multi-hop reasoning.
Let's build your data engine.
Schedule a technical call with our integration team to discuss your current pipeline bottlenecks and specific foundation model requirements.