01
Design
Solution architecture, model selection, data flow, evaluation criteria and failure behavior.
Output: A technical design with the success threshold defined before anything is built.
AI Engineering
AI inside your CRM, ERP and workflows.
We Build generative, agentic and predictive systems into the platforms your teams already use, with the integration, evaluation and monitoring that keep them working after launch.
What is
AI Engineering?
AI engineering is the practice of building, integrating, and deploying artificial intelligence systems into production software. It covers generative AI applications, agentic systems, machine learning models, and the integration work that connects them to existing platforms and workflows.
The distinction from data science is deployment: AI engineering service ends with a system in use, not a model in a notebook.
The AI Implementation Gap
95%
Enterprise GenAI initiatives fail to show measurable P&L impact.
MIT NANDA — The GenAI Divide: State of AI in Business 2025
5%
Custom enterprise GenAI solutions successfully reach production.
MIT NANDA — The GenAI Divide: State of AI in Business 2025
2×
External AI implementation approaches achieved higher deployment success than internal builds.
MIT NANDA — The GenAI Divide: State of AI in Business 2025
Signs You Need AI Engineering
- A Working Prototype with No Adoption
- A Fine Model Without the Infrastructure Around It
- Engineers Learning AI Mid-Delivery
- Integration Required into Existing Systems
- Inconsistent Output with No Explanation
- A Validated Use Case Against a Deadline
What AI Engineering Covers
LLM-powered applications, retrieval-augmented generation (RAG), AI assistants and copilots. As a generative AI development company, we design prompts, connect AI with your data, improve response accuracy, and build evaluation systems to measure and improve output quality.
Systems that plan and act rather than answer. Tool use, multi-step workflows, multi-agent orchestration, and the human-in-the-loop checkpoints that keep autonomous systems accountable.
Forecasting, classification, recommendations, anomaly detection, and scoring models. Build the data pipelines, select and train the right models, and continuously improve performance as new data becomes available.
Language and perception. Document extraction, classification, summarization, conversational interfaces, image and video analysis, defect detection, and OCR.
Building the product around the model, interface, workflow, permissions, audit trail and the fallback behavior for when the model gets it wrong. Full-stack delivery, not a model endpoint.
Connecting AI to the systems you already run. CRM, ERP, support platforms, internal tools and data sources, through APIs, event pipelines and existing authentication. This is where most AI projects stall and where most of the value sits.
How We Build AI Systems
This service runs the middle of The Pivot. Where AI applies is settled through AI Strategy & Roadmap. Governance and monitoring after launch run through AI Operations.
What You Walk Away With
Working software in production, with everything needed to run it after we leave.
A Production AI System- An Evaluation Harness
- Integration into Your Existing Stack
- Technical Documentation and Runbooks
- A Monitoring and Alerting Setup
- Source Code and Model Artifacts
See Us in Action
SaaS & Technology
Cutting Support Response Times by 40% with an Enterprise Knowledge Assistant
Our Technology Landscape
Model platforms
- OpenAI
- Anthropic
- Azure OpenAI
- AWS Bedrock
- open-weight models
Frameworks and orchestration
- LangChain
- LlamaIndex
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face
Data and retrieval
- PostgreSQL
- pgvector
- Pinecone
- Snowflake
- Databricks
- Apache Kafka
Application and infrastructure
- Python
- TypeScript
- FastAPI
- React
- Docker
- Kubernetes
- AWS
- Azure
- Google Cloud
Evaluation and observability
- Automated evaluation harnesses
- LangSmith
- model registries
- prompt versioning
- cost and latency monitoring
Common questions
A focused first use case typically takes 8 to 14 weeks from design to live. Integration complexity drives the range more than the AI work does. Connecting to legacy systems takes longer than building the model layer.
Retrieval-based systems work with messier data than most people expect. If the readiness assessment finds a genuine gap, the data work gets sequenced before the build rather than discovered during it.
Buy where the workflow is standard. Build where the process is specific to how your business works, or where the data cannot leave your environment. We will tell you when buying is the better answer.
Grounding against your own data, structured output constraints, evaluation harnesses that run on every change, and human review on anything consequential. Quality is measured continuously, not assessed once at launch.
You do. Source code, model artifacts and documentation sit in your repositories. The system is built so your team can operate it without us.
Have a Use Case Ready to Build?
Bring us the problem, the data, and the deadline. We will tell you what it takes to get it into production.
Talk to Our engineer