Fit check
Confirm one workflow, the tools involved, the data shape, and whether software is likely to pay off.
I build secure search, intelligent routing, and database middleware using local models deployed entirely inside your own private cloud infrastructure. Built at startup speed, engineered for enterprise-level security.
100% data sovereignty. Engineered to pass enterprise security and SOC 2 audits.
Confirm one workflow, the tools involved, the data shape, and whether software is likely to pay off.
Repair or build one narrow workflow, search flow, dashboard, or integration.
Ship a search, routing, reporting, admin, or internal-tool workflow when scope is clear.
Local RAG, agent-style workflows, agency, and MVP work can price higher when private deployment, data access, approvals, auth, or product scope adds risk.
Teams derisking complex B2B integrations before dedicating internal developer resources.
SaaS teams delivering custom post-sales connections or strict transactional ledger syncs.
Engineering teams looking to compress timelines, refactor loops, or stabilize transaction pipelines.
Every build sprint includes documentation, source code ownership, and 30 days of build integrity support. If your build fails to run on the configured hardware per the setup guides, I work until it launches at zero additional cost.
I can be the implementation partner for agencies and operators who need AI search, workflow software, dashboards, or internal-tool builds without hiring a full internal technical team.
DISCUSS PARTNERSHIPThe build works with your CRM, docs, email, support queue, billing system, database, or internal admin app. It can run local models, private VPC services, or approved managed endpoints without replacing core software.
Models are not allowed to quietly guess. Structured records are queried deterministically; weak retrieval, missing fields, ambiguous inputs, and risky actions route to review so operational data stays intact.
Builds utilize highly popular, standard technologies (TypeScript, Node.js, PostgreSQL, Docker). No custom closed-source frameworks or paid runtime dependencies are introduced.
We structure retrieval using semantic chunking with parent-document overlap, HNSW vector database indexing for sub-millisecond database queries, and optimized quantized models (e.g. GGUF runtimes) to match hardware limits.
The stack is chosen for lean teams, not for hype. Local open-weight inference can remove token margin where volume or privacy justifies the infrastructure.
Build options support private VPC deployment, strict local inference, and zero-retention API keys. No model provider is ever permitted to use your operational data for training.
| Deployment Target | Monthly Cost | Hardware / Provider Setup | Data Privacy Level |
|---|---|---|---|
| Air-Gapped Node Spec 02 | $0 / mo | Runs 100% locally on existing hardware you own (e.g. Apple Silicon Mac or GPU workstation) | Absolute isolation (no data leaves the physical site) |
| Private VPC Instance Spec 01 / 03 | $50 - $150 / mo | AWS EC2, GCP Compute Engine, or Hetzner Cloud virtual instances | High privacy (isolated runtime, no retention policy logs) |
| Hybrid Managed LLM | Token Rates | Managed LLM endpoints (e.g. OpenAI/DeepSeek) with pay-per-token direct billing | Standard (secured via zero-data retention APIs) |
Submit a quick manual flow for an async fit-check. You will receive an honest verdict on whether automation pays off, private local AI is justified, or if you should stay manual.
START FREE FIT-CHECK