Your competitors are shipping AI features. Your board is asking about your AI
strategy. Your team is prototyping in notebooks but nothing is reaching
production. We fix the gap between AI ambition and AI delivery — with dedicated
ML engineers and AI developers who’ve shipped LLM integrations, intelligent
assistants, and automation pipelines for SaaS products like yours.
The gap between a working Jupyter notebook and a production AI feature is enormous. It requires ML engineering, infrastructure, monitoring, and product thinking — not just data science. We bridge that gap with teams who've done it before.
Fine-tuning vs. RAG vs. prompt engineering vs. traditional ML — each has different cost, accuracy, and latency tradeoffs. We assess your data, your users, and your constraints before writing a single line of code.
So are we. Enterprise AI requires guardrails, evaluation frameworks, and monitoring that most tutorials skip. We build AI systems with production-grade safety from day one.
You don't need to. Our dedicated AI engineers embed into your existing team and transfer knowledge as they build. You get production AI features without permanent ML headcount.
We don't start with models. We start with the business problem. Every AI engagement begins with a 2-week Discovery & Assessment sprint where we map your use case to the right technical approach. We evaluate your data readiness, define success metrics, and architect a solution before any model training begins.
Then we build in production-first sprints — not research cycles. Every two weeks, you see working software, not slide decks. Our AI engineers pair with your product and engineering teams to ensure what we build integrates cleanly into your existing architecture.
The result: AI features that ship on time, perform reliably, and deliver measurable business impact.
We integrate large language models into your product — whether that’s OpenAI, Anthropic Claude, Mistral, Llama, or fine-tuned open-source models. From intelligent search and content generation to document understanding and conversational interfaces, we build LLM-powered features that users actually rely on.
Common deliverables: RAG pipelines, semantic search, AI writing assistants, document intelligence, automated summarization, AI-powered customer support.
We build AI assistants that go beyond scripted flows. Our chatbots understand context, access your internal knowledge bases, take actions through API integrations, and escalate intelligently when they reach their limits.
Common deliverables: Customer support bots, internal knowledge assistants, onboarding copilots, sales qualification agents.
For problems where LLMs aren’t the answer — churn prediction, fraud detection, recommendation engines, demand forecasting — we build and deploy traditional ML models with proper feature engineering, training pipelines, and monitoring.
Common deliverables: Recommendation engines, churn/LTV prediction, anomaly detection, classification systems, scoring models.
Models in production need monitoring, retraining, versioning, and governance. We build the infrastructure that keeps your AI systems reliable as data drifts and usage scales.
Common deliverables: ML pipeline orchestration (Airflow, Kubeflow), model monitoring, A/B testing frameworks, feature stores, model registries.
Not everything needs a neural network. We build intelligent automation using a combination of AI, business rules, and API orchestration to eliminate manual work across your operations.
Common deliverables: Document processing pipelines, automated QA workflows, intelligent routing, email/ticket classification, data extraction automation.
OpenAI GPT-4, Anthropic Claude, Mistral, Llama, Hugging Face, LangChain, LlamaIndex, Pinecone, Weaviate, ChromaDB
PyTorch, TensorFlow, scikit-learn, XGBoost, Spark MLlib
MLflow, Kubeflow, Airflow, Weights & Biases, DVC, BentoML
AWS SageMaker, Google Vertex AI, Azure ML, Docker, Kubernetes
Snowflake, BigQuery, PostgreSQL, Redis, Apache Kafka
We audit your data, map your use case, evaluate technical approaches, and produce a detailed implementation plan with architecture diagrams, cost estimates, and a timeline.
We build a working proof of concept against your actual data. This validates the approach before we invest in production engineering.
Iterative 2-week sprints. Each sprint delivers working features integrated into your product. Continuous testing, evaluation, and refinement.
Post-launch monitoring, model retraining, performance optimization, and feature expansion as your needs evolve.
A B2B SaaS platform needed to add AI-powered document analysis to process 50,000+ insurance documents monthly. Their team had no ML expertise.
We embedded a 3-person AI team (ML engineer, backend developer, QA) for 14 weeks. Built a RAG pipeline using LangChain and OpenAI with a Pinecone vector store, connected to their existing React/Node.js application.
- Document processing time reduced from 12 minutes to 45 seconds per document
- 94% accuracy on data extraction tasks
- Feature shipped 3 weeks ahead of schedule
- Now processing 60,000+ documents monthly
A proof of concept usually takes 2–4 weeks. A production-ready AI feature typically takes 8–16 weeks depending on complexity, data readiness, and integration requirements. We’ll give you a specific timeline after our Discovery sprint.
Not necessarily. Our Discovery sprint includes a data readiness assessment. We can help you organize, clean, and structure your data as part of the engagement if needed.
It depends on your use case, budget, latency requirements, and data privacy needs. We’re provider-agnostic — we’ve shipped production features with OpenAI, Anthropic, Mistral, and open-source models. We recommend the right tool for your specific situation.
We follow enterprise security practices: data encryption at rest and in transit, role-based access controls, audit logging, and — where required — on-premises or VPC-isolated deployments. For healthcare clients, we follow HIPAA-compliant development practices.
Consultants give you a strategy deck. We give you working software. Our teams don’t just advise — they write code, build pipelines, and ship features alongside your engineers.
Yes. We integrate into your existing tech stack, version control, CI/CD pipeline, and project management tools. We adapt to your workflow, not the other way around.
AI project costs depend on scope, team size, and duration. Typical engagements range from $15,000–$60,000 for a focused feature build, or $8,000–$20,000/month for ongoing dedicated team capacity. We provide detailed estimates after our Discovery sprint.
Let’s talk about your use case. Our Discovery calls are free, technical,
and zero-pressure — we’ll tell you honestly whether AI is the right
investment for your product right now.
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