RAG Pipelines
Retrieval systems that connect LLMs to your own data. Document ingestion, chunking, embeddings, vector storage, retrieval tuning. Most of the work is deciding what the model gets to read before it answers.
AI Infrastructure.
Most AI projects die somewhere between the demo and the deploy. We build the layer that gets them across: data pipelines, retrieval, orchestration, evals, and monitoring that catches drift before your users do.
01Overview
The plumbing under the demo.
AI infrastructure is the complete technical stack underneath the chat window: ingestion, retrieval, orchestration, evals, monitoring. The model itself is maybe ten percent of it.
Most teams meet AI at the surface: a widget, a demo, a proof of concept. The distance between that and genuine business value is unglamorous: data ingestion that doesn't choke on messy PDFs, indexes that stay current, fallback logic, cost caps, and someone actually watching the dashboards.
We design and build that entire stack, and we treat it as an engineering discipline. (Every agency says it does AI now. Ask them how they run their evals.)
The result is AI that behaves like the rest of your business systems: predictable, observable, and boring in the best possible way.
RAG
Retrieval pipelines that answer from your own documents, current, cited and specific to your domain
LLM
Orchestration across OpenAI, Anthropic, and open-source models, routed by task and cost, with fallbacks for provider outages
MEM
Long-term memory and context management, so the system still knows who it's talking to by the third question
OPS
Tracing, cost dashboards, and eval suites that catch quality drift before your users open a support ticket
02What we offer
What we build.
LLM Orchestration
Multi-step workflows with tool use, function calling, and model routing. We build the logic layer that turns a raw model call into a dependable business process: fallbacks, retries, timeouts, cost controls.
AI Agents
Systems that plan and execute multi-step tasks: browsing, writing, calling APIs, managing data. We scope what an agent is allowed to touch, add human checkpoints where money or reputation is on the line, and log everything.
Knowledge Systems
Structured knowledge bases, entity graphs, and semantic search. We turn the folder nobody wants to open (PDFs, emails, spreadsheet exports) into a knowledge layer your systems can actually query.
AI-Native APIs
Backend APIs built for machine callers: structured outputs, streaming responses, tool schemas, semantic endpoints. The kind of interface an agent can hit ten thousand times a day without surprising anyone.
Observability & Evals
LLM tracing, cost dashboards, eval pipelines, regression tests. Unmonitored AI in production is a liability with a monthly invoice. We'd rather you see the problem before your customers do.
03How it works
How we deliver.
01
Architecture First
Before anyone touches an API key, we map your data, define the retrieval strategy, and design the system architecture. Getting this right decides everything downstream: speed, cost, accuracy.
02
Data Preparation
We clean, structure, and embed your data into vector stores with chunking strategies that fit the material. Most AI projects fail right here: badly prepared data produces confidently wrong answers. So this is the phase we refuse to rush.
03
Core System Build
RAG pipelines, orchestration layers, API design, and integration work, built iteratively against eval checkpoints. Quality gates run at every stage.
04
Evaluation & Tuning
Systematic testing of retrieval accuracy, response quality, and latency. We measure before we ship, and we write the numbers down so the next improvement has something to beat.
05
Production Deployment
Containerized deployment with monitoring, alerting, and cost controls. We deploy, watch the dashboards and stay with you through the first weeks of production.
NextCustomer experience