Turning Ambiguity
into Intelligence
Expert AI consulting, LLM integrations, and workflow automation solutions that transform your operations and drive measurable business outcomes.
Expert AI Solutions
Comprehensive AI services tailored to accelerate your digital transformation
AI Consulting
Strategic guidance on AI adoption, from assessment to implementation roadmaps.
Workflow Automation
End-to-end automation of business processes using intelligent AI agents.
LLM Integration
Seamless integration of large language models into your existing systems.
Custom AI Agents
Purpose-built AI agents designed for your specific business needs.
Post-Training Data That Ships
Enterprise-grade AI training data built from real production codebases, validated through multi-turn expert evaluation
Supervised Fine-Tuning (SFT)
SFT data across a variety of coding tasks including bug fixes, refactors, and feature implementations from real production repositories.
Reinforcement Learning Environments
RL environments designed for repo-wide code evaluation and verification tasks with structured reward signals.
RLHF
Custom model endpoint in-the-loop RLHF with expert human preference rankings across multi-turn coding interactions.
The artifact download endpoint currently serves files without setting Content-Type or Content-Disposition headers. Clients have no way to determine file types or suggested filenames. Fix the download handler to detect MIME types from file extensions and set both headers. Handle missing extensions with a sensible fallback. Extract basenames correctly from nested artifact paths. Add test coverage for known types, unknown types, and edge cases.
# Fix: Add MIME type detection and download headersdef _send_artifact(artifact_path): basename = posixpath.basename(artifact_path) mimetype, _ = mimetypes.guess_type(basename) if mimetype is None: mimetype = "application/octet-stream" return send_file( artifact_path, mimetype=mimetype, as_attachment=True, download_name=basename )Model B slightly better
Uses Flask idiomatically instead of manual header manipulation
Before any dataset reaches your hands, it passes through a final internal review layer. This is our last line of defense — a dedicated quality gate that catches edge cases the primary evaluation may have missed.
Blind Re-Review
A second expert reviews every evaluation independently, without seeing the first reviewer's scores, to eliminate individual bias.
Consistency Audit
Automated checks flag scoring outliers, incomplete rubric fields, and prompt-to-response misalignments before anything leaves the pipeline.
Senior Calibration
A senior technical lead samples batches for calibration, ensuring rubric interpretation stays uniform across all reviewers and domains.
Final Sign-Off
Only after passing all gates does the data enter the delivery artifact. Every record ships with a provenance trail linking back to the original PR.
Every record in the final deliverable is double-reviewed, audited, and signed off before it reaches you.
Impact
How AI Transforms Business
Measurable results from AI implementations across industries
Weekly Data Processing
Process Accuracy
Team Required
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