Product Development
AI & ML Development
Generative AI features, chatbots and agents, LLM integrations, and the MLOps to keep them reliable in production.
The Problem
Most "AI features" ship as a demo, then fail in production: hallucinated answers, no evaluation pipeline, and cost curves nobody modeled before launch.
Our Approach
We treat AI features like production software: retrieval grounding, evaluation sets, cost monitoring, and fallback behavior are part of the build, not an afterthought.
What's Included
Generative AI features
Product-embedded, not a chat widget bolt-on
Chatbots & agents
Task-scoped, with clear escalation paths
LLM & RAG integration
Fine-tuning, prompt pipelines, retrieval
MLOps & data engineering
Pipelines, versioning, retraining
Evaluation & guardrails
Test sets, confidence thresholds
Cost & latency monitoring
Modeled before rollout, tracked after
Vector database setup
Pinecone, pgvector, or self-hosted
Human-in-the-loop review
For the decisions that need it
Our Process for This Service
01
Use-Case Scoping
Where AI helps, and where it doesn't.
02
Data & Retrieval Design
Sources, grounding, vector setup.
03
Build & Evaluate
Iterate against a real test set.
04
Pilot Rollout
Limited exposure, cost & quality tracked.
05
Scale & Monitor
Full rollout with ongoing guardrails.
Tech Stack We Use
Python
PyTorch
LangChain
OpenAI / Anthropic APIs
Pinecone / pgvector
Airflow
AWS SageMaker
Docker
Engagement Models
Most clients start with a fixed-scope pilot before a full rollout.
Fixed-Scope Pilot
Validate one use case end-to-end before committing further.
Time & Materials
Best for iterative model & prompt development.
Dedicated Squad
Ongoing MLOps & feature ownership at scale.
Frequently Asked Questions
Yes, most engagements integrate into an existing codebase rather than starting fresh.
Retrieval grounding, evaluation test sets, and confidence thresholds with human fallback where it matters.
Whichever fits the use case and budget (OpenAI, Anthropic, or open-source), evaluated against your data, not a default.
Usage monitoring and caching from day one, with cost projections before you commit to a rollout.
Yes, data handling, retention, and vendor agreements are scoped explicitly, and NDAs apply to all training/context data.
Yes, a fixed-scope pilot is the most common way clients validate an AI feature before a full build.
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