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AI & data systems

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.

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Capabilities

What we build

Four ways we put models to work on your own data: retrieval, prediction, language, and vision, engineered for production, not demos.

RAG & Private LLMs

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

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Predictive ML

Turn history into forecast. Custom regression and classification models predict demand, price dynamically, and flag anomalies before they reach operations.

Forecasting
Classification
Anomaly Detection

Discuss this

NLP Pipelines

Automate text-heavy work. Semantic search, sentiment, and entity extraction run across thousands of unstructured documents in real time.

Semantic Search
Sentiment
Entity Extraction

Discuss this

Vision & OCR

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

Discuss this

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

Discuss this

Turn history into forecast. Custom regression and classification models predict demand, price dynamically, and flag anomalies before they reach operations.

Forecasting
Classification
Anomaly Detection

Discuss this

Automate text-heavy work. Semantic search, sentiment, and entity extraction run across thousands of unstructured documents in real time.

Semantic Search
Sentiment
Entity Extraction

Discuss this

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

Discuss this

Process

AI delivery process

A disciplined, transparent path from initial scope to production handoff, so you always know what is shipping and when.

01
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.
02
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.
03
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.
04
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.

Stack

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.

Delivery

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.

FAQ

Frequently asked questions about AI development

Pricing, process, ownership, and technical answers for AI development projects.

$15K–$80K for most custom builds; AI automation workflows start lower, around $8K–$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 30-minute call. Our CEO 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.

Free consultation

See What an Engineer Could Take Off Your Plate

Book a free 30-minute call. We look at how your team works, point to what is worth automating first, and you leave with a clear next step, whether we work together or not.

Book a Free 30-Minute Call

We map one workflow that eats your team’s week and show you what an engineer and a few AI agents could take off it. No slide deck, no sales pitch. Just a working session.

Don’t want a call? Email [email protected]

Book a free call

One workflow mapped with you, live
A clear first step for your team
Reply within one working day

We worked with Seif on the React Native app for our vehicle-appraisal platform. What stood out most was how much ownership he takes: he thinks a feature through, raises edge cases before they turn into bugs, and delivers something that actually works. And then there is one more thing why I wanted to work with Seif after our very first call: Exceptional clear and consistent communication. It is a pleasure to work with him and his team!