Data Engineering
AI is only as capable as the data feeding it. We audit, clean, and vectorize your legacy databases, establishing the high-fidelity pipelines required for accurate retrieval.
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AI Software Development
Production AI, built on your own data
We architect, train, and deploy secure ML models and custom LLM integrations that run reliably on your proprietary data. No generic wrappers, no data leaking to public models.
What we build
Four ways we put models to work on your own data: retrieval, prediction, language, and vision, engineered for production, not demos.
Frontier reasoning grounded in your own data. We connect foundation models to private vector stores so answers cite your documents instead of hallucinating.
- RAG Pipelines
- Vector DBs
- Zero Retention
Turn history into forecast. Custom regression and classification models predict demand, price dynamically, and flag anomalies before they reach operations.
- Forecasting
- Classification
- Anomaly Detection
Automate text-heavy work. Semantic search, sentiment, and entity extraction run across thousands of unstructured documents in real time.
- Semantic Search
- Sentiment
- Entity Extraction
Extract structure from images and PDFs. OCR and computer-vision pipelines parse non-standard documents and run quality control on the line.
- OCR
- Document Parsing
- Inspection
AI delivery process
A disciplined, transparent path from initial scope to production handoff, so you always know what is shipping and when.
Model Selection
We evaluate commercial APIs against private open-source alternatives (like Llama or Mistral) to establish the optimal balance of inference speed, operational cost, and strict data privacy.
System Fine-Tuning
We align the models to your specific business logic and implement strict programmatic guardrails (system prompts and safety filters) to prevent hallucinations and assure output reliability.
Deploy & hand over
We deploy to secure, scalable cloud infrastructure with logging, access controls, and clear runbooks, then hand over full ownership so your team can operate and extend it.
The AI stack
The models, retrieval, and serving layers we reach for, chosen per workload rather than by habit.
Retrieval & vectors
Embeddings, hybrid search, and semantic caching over pgvector and Redis, for retrieval that stays fast and grounded in your data.
Serving & inference
Containerized inference behind typed APIs, autoscaled on cloud GPUs with strict latency budgets.
Data pipelines
Ingestion, cleaning, and vectorization that keep the knowledge base fresh without manual work.
How we build & ship
The workflow behind every build: an AI-native toolchain, automated delivery, and guardrails that keep quality high.
Ship & version
Every change is versioned, reviewed, and shipped through automated pipelines, with fast rollback if a release regresses.
AI-native engineering
Senior engineers build with an AI-native toolchain, shipping more in less time without lowering the bar on architecture or review.
Guardrails & evals
Schema-validated outputs, programmatic guardrails, and eval suites that catch regressions before your users do.
Frequently asked questions about AI development
Pricing, process, ownership, and technical answers for AI development projects.
$15K to $80K for most custom builds; AI automation workflows start lower, around $8K to $20K. We don’t bill hourly. We scope a fixed price per phase, so you know the total before signing anything.
Fixed price, per phase. We break the work into scoped phases, quote each one, and hold to it. If scope changes mid-build we re-quote the remaining phases and you approve before we continue. No surprise invoices.
Book a 20-minute call. Our founder talks through your project, gives you a timeline and price range, and is straight about whether we’re the right fit. No pitch deck, no sales funnel.
No. We enforce absolute data isolation. We utilize enterprise-tier APIs with strict zero-data-retention policies, or we deploy dedicated, self-hosted open-source models inside your own Virtual Private Cloud (VPC).
RAG connects an AI model to a secure database of your files, allowing it to read your data before answering. Fine-tuning actually alters the model's internal neural weights. We typically architect RAG pipelines for B2B knowledge retrieval, as it is highly accurate, easily updatable, and mathematically reduces hallucinations.
We implement semantic caching layers, strict rate limiting, and intelligent model routing. Complex reasoning queries go to large models and simple, repetitive queries go to cheaper, faster micro-models to drastically optimize your cloud expenditures.
Explore more services
Adjacent capabilities from the same senior team, for when your roadmap goes further.
Let's build your project
Book your free 20 minute product discovery call with us.
Looking forward to meet you.
Prefer email? [email protected]