Professional AI/ML Solutions
Professional engineering services to transition AI prototypes to production-grade systems.
We provide the engineering expertise to bridge that gap, deploying defensible architectures into your own ecosystem.
For early-stage startups, this means launching customer-facing AI products with:
- Fast MVP delivery that proves product-market fit.
- Secure data and API integration for trusted user workflows.
- Production-ready reliability and handover for your team.
Quick Discovery
Answer one quick question to see which service path is best for your startup.
Best fit: The Implementation Sprint for fast, secure AI product alpha deployment.
How We Engage
Choose the engagement that matches your startup stage: launch an AI MVP, build reliable data foundations, or harden an existing AI workflow.
The Implementation Sprint
Moving from architectural blueprints to a live, secure environment with speed.
The Goal: Deploying a Private-Cloud Alpha
Scope of Work:
- Core Build: Deployment of our pre-validated RAG or Agentic frameworks (LangGraph/Cyclic DAGs) customized for your proprietary data.
- Private Cloud Integration: Deployment into your AWS, Azure, or GCP environment to ensure data residency and security.
- Security First: Infrastructure as Code setup to ensure all data processing remains within your secure VPC.
- Proprietary Data Ingestion: Connection to your specific internal data stores and API ecosystems.
Targeting a functional Alpha deployment in 14 business days, contingent on infrastructure readiness.
Data Pipeline Engineering
Transform raw, unstructured data into high-fidelity assets for your LLMs.
The Goal: High-Quality Structured Data
Scope of Work:
- Data Structuring & Cleaning: Transforming messy PDFs, documentation, and legacy logs into optimized Markdown or JSON formats for model consumption.
- Automated PII & Anomaly Detection: Implementing automated layers to detect and redact sensitive information before it reaches the model API.
- Semantic Chunking & Indexing: Designing sophisticated data ingestion pipelines that optimize how information is retrieved during the RAG process.
Targeting initial pipeline architecture and data cleaning protocols established within 5 business days. Implementation depends on dataset volume.
Reliability & Security Audit
Audit cost, latency, and reliability of your AI pipelines.
The Goal: Optimizing Existing AI Pipelines
Scope of Work:
- Performance Tuning: Systematic reduction of inference latency and token expenditure through quantization (GGUF/AWQ) and prompt-chain optimization.
- Evaluation Framework: Implementing RAGAS and custom LLM-as-a-judge metrics to quantify system faithfulness.
- Reliability Hardening: Building deterministic guardrails to eliminate hallucinations in mission-critical workflows.
Targeting comprehensive audit and optimization reports delivered within 7 business days. Details depend on the system infrastructure.
Service Examples
Engineering services designed around production AI capabilities that startup teams can ship quickly.
Use Cases
Common product use cases we help early-stage startups bring to market.
The Process
Step 1. Technical Briefing
60-minute technical discovery call. You can expect:
- Evaluate your current data infrastructure and cloud readiness.
- Specify technical stack (model selection, RAG vs. Fine-tuning etc) for your business objectives.
- General Q&A addressing security, latency expectations, integration constraints, and more.
Step 2. Project Scoping
After our briefing, I provide a formal Statement of Work (SOW). You can expect:
- Deliverables like a list of features, agents, and integrations.
- Timeline and milestones from the kickoff to deployment.
- Fixed quote.
Step 3. Execution
After SOW, project kick-off with direct engineering access. You can expect:
- A mid-point review to refine reasoning and tool-calling logic before finalization.
- Full handover of production-ready source code and private cloud deployment.
- Documentation for your team to ensure long-term maintenance and monitoring.