AI & Data Engineering
AI development company building agents, RAG copilots, and data pipelines. LLM integration services for Claude, GPT, Gemini, and open-weight models.
Ask ten companies about AI right now and you'll hear the same story: everyone's experimenting, almost nobody has it in production. That's the gap we close. As an AI development company, we take teams from a readiness audit to systems doing real work: copilots that answer from your own documents, agents that handle actual workflows, and the data engineering underneath that makes both trustworthy. And if a rules engine or a well-built dashboard solves the problem, we'll say so before you spend money on machine learning.
Data foundations come first, because model quality is capped by data quality. We build ETL and ELT pipelines in Airflow, Spark, and dbt that turn scattered spreadsheets, app databases, and third-party exports into a clean, queryable warehouse. On top of that sits the retrieval layer: a RAG implementation on pgvector or Pinecone that lets a model answer questions with citations, so responses stay grounded in your data instead of the model's guesses.
The most requested work now is agentic. AI agents for business workflows don't just answer questions, they take actions: reading the ticket, looking up the order, drafting the refund. We connect agents to your CRM, databases, and internal APIs through the Model Context Protocol (MCP), the open standard for tool integrations, and we put human-in-the-loop approvals in front of anything consequential. An agent that proposes and a person who approves beats both the fully manual process and the fully autonomous one.
Quality is measured, not assumed. Every LLM feature we ship carries an eval suite built from your real cases, releases are gated on pass rates, and tracing keeps watching quality and cost in production. Deployment happens inside your own cloud account or on your own hardware with open-weight models when data can't leave, which is what makes enterprise AI adoption survivable for legal and compliance, not just exciting for the demo.
End-to-end delivery
AI Readiness Audit
A 2 to 3 week assessment of your data, systems, and use cases. You get a ranked list of AI opportunities with effort, run cost, and risk attached to each, so the first project you fund is the right one.
LLM & RAG Integration
Retrieval-augmented assistants that answer from your own documents with citations, built on LangChain or LlamaIndex with pgvector or Pinecone, deployed inside your cloud account.
AI Agents & Workflow Automation
Agentic systems that take actions, not just answer questions: triaging tickets, reconciling records, drafting responses. Every consequential action routes through a human-in-the-loop approval before it executes, and everything gets logged.
MCP & Tool Integrations
We connect models to your CRM, databases, and internal APIs through the Model Context Protocol, so one integration works across Claude, GPT, and whatever model you switch to next year.
AI Copilots for Internal Teams
Assistants wired into the tools your people already use: support replies drafted from your knowledge base, sales answers pulled from your product docs, engineering questions answered from your own codebase.
Fine-Tuning & Model Adaptation
Most problems are solved with better prompting and retrieval, and we'll tell you when that's true. When fine-tuning earns its cost, we run LoRA and full fine-tunes on open-weight models like Llama and Mistral, trained with Hugging Face and served on vLLM.
Evals & LLM Observability
Eval suites built from your real cases that gate every release, plus tracing, cost tracking, and quality monitoring in production. If answers get worse, you find out before your customers do.
Guardrails & AI Governance
Input and output filtering, PII redaction, prompt injection defenses, audit logs, and usage policies, so legal and compliance can sign off before launch instead of blocking it after.
Data Engineering Foundations
Reliable ETL and ELT pipelines in Airflow, Spark, and dbt that feed your models, your retrieval layer, and your dashboards from one clean, queryable warehouse.
Predictive Models & MLOps
The classic machine learning practice: demand forecasting, churn prediction, document extraction and OCR, shipped with model versioning, drift monitoring, and retraining pipelines rather than one-off notebooks.
How we build
Readiness Audit
Assess your data, systems, and use cases, then rank AI opportunities by value against effort and risk.
Data Foundations
Pipelines and the retrieval layer first, because model quality is capped by data quality.
Prototype with Evals
A working prototype and an eval suite built together, so 'is it good enough' gets a measured answer.
Production Hardening
Guardrails, access controls, cost controls, and failure handling before real users touch it.
Observe & Iterate
Tracing, drift detection, and eval-gated releases keep quality improving after launch, not decaying.
What makes this work
Production-grade, not notebooks
Every model ships with monitoring and a retraining pipeline.
Domain-specific tuning
Models trained and validated against your actual data, not generic benchmarks.
Explainability built in
Where it matters (healthcare, finance), we build in interpretability, not just accuracy.
Realistic scoping
We'll tell you when a rules engine beats a model. We're not incentivized to oversell AI.
What working with us looks like in numbers
From readiness audit to a working prototype with eval numbers you can make a build decision on.
Copilots we ship answer from your own documents with citations, so every response traces back to a source.
LLM features release only after passing an eval suite on your real cases, and the same checks keep running in production.
Extraction accuracy targets on document processing pipelines before we call them done.
Typical drop in reporting latency once pipelines replace manual exports.
Ships with drift monitoring and a retraining pipeline, not a notebook handover.
Tools we actually use
AI & Data FAQs
Honest answer: it depends, and it changes. We benchmark two or three candidates against your actual use case during prototyping and design the integration so the model stays swappable. Frontier models like Claude, GPT, and Gemini win on reasoning-heavy work; open-weight models like Llama and Mistral win when cost or data residency rules the decision. Locking into one model in 2026 is a bet you don't need to make.
Yes. Most of our deployments already run inside the client's own AWS or Azure account. Where data can't touch an external API at all, we serve open-weight models on your own GPUs with vLLM, entirely inside your network. Private deployment usually costs more per token, and we'll show you that math before you choose.
Three layers. RAG grounds answers in your own documents and returns citations. Eval suites measure accuracy on real cases before every release. Guardrails catch out-of-policy output in production. None of that makes hallucination impossible, so we also scope features to what can be verified against a source, and we're upfront about what can't.
Software that uses a model to decide and act, not just reply. In a real workflow that looks like: read the support ticket, look up the order in your database, draft the refund, then wait for a person to approve it. The approval step is the point. Agents propose, humans keep authority over anything consequential, and every action is logged.
A readiness audit runs 2 to 3 weeks. A grounded copilot or RAG prototype with its eval suite typically takes 4 to 6 weeks, and you make the build call on those eval numbers. Production hardening adds another 4 to 8 weeks depending on integrations, guardrails, and compliance requirements.
No. We use enterprise API tiers or your own cloud accounts, where providers are contractually barred from training on your data, and data processing agreements go in place before anything flows. When the data is sensitive enough, we deploy privately so it never leaves your environment at all.
It depends on volume, but it's designed, not discovered. We route routine requests to smaller, cheaper models, cache repeated answers, and batch whatever doesn't need to be real-time. You get a monthly run-cost estimate before we build, and per-feature cost tracking after launch, so the bill never becomes a mystery.