Available for new projects

AI engineering for teams shipping to production.

Jabrod builds RAG pipelines, LLM agent systems, and the infrastructure around them — fixed scope, fixed price, working remotely with teams in the US and UK. We build our own product too, but if you want it built for you, that starts here.

See what we build
Or email us directly — contact@jabrod.com

80,000+

users served by AI systems we've built and run in production

40%

cut from LLM inference spend on a live platform, without quality loss

2 wks

from kickoff to a working, evaluated retrieval pipeline

Trusted by Fast Growing Startups

Juristo AI Labs
Aceternity
TechFlow
DataSync
CloudBase
ScalePoint
InnovateLabs
FlexTech
NextGen
Juristo AI Labs
Aceternity
TechFlow
DataSync
CloudBase
ScalePoint
InnovateLabs
FlexTech
NextGen
Juristo AI Labs
Aceternity
TechFlow
DataSync
CloudBase
ScalePoint
InnovateLabs
FlexTech
NextGen
Juristo AI Labs
Aceternity
TechFlow
DataSync
CloudBase
ScalePoint
InnovateLabs
FlexTech
NextGen

What we do

Fixed scope. Fixed price. No discovery phase.

You know what you are getting, what it costs and when it lands before you commit to anything.

LLM Cost Audit

$500fixed

1 week · remote

Where your inference spend actually goes, quantified, with a ranked list of fixes.

See what is included

RAG Pipeline Sprint

$2,000fixed

2 weeks · fixed scope

A retrieval system over your documents, with citations and an evaluation set, in your stack.

See what is included

RAG Eval & Repair

$1,200fixed

10 days · fixed scope

Find out why your retrieval gives confident, wrong answers — and ship the fixes.

See what is included

Selected work

Numbers, not adjectives.

40%

Cutting inference cost on a live legal-AI platform

The problem
A production legal assistant serving 80,000+ users was routing every query, trivial lookups included, through a frontier model, with no caching layer and retrieval returning far more context than answers required.
What we did
Introduced prompt caching across the highest-volume paths, and built a routing layer that classifies each query and sends the simple majority to lighter models, keeping the frontier model for work that genuinely needs it.
Result
A 40% reduction in inference spend, with no measurable drop in answer quality.

4-stage

A cited retrieval pipeline at production scale

The problem
Legal answers are worthless if users can't check them. The system needed to ground every response in source documents and survive real traffic.
What we did
Designed a four-stage pipeline (document parsing, chunking, embedding, retrieval) behind a multi-agent routing layer with explicit reasoning stages that dispatch each query to the right tools and workflows.
Result
Context-grounded, citable answers running in a single production system alongside auth, background jobs and frontend delivery.
Also from Jabrod

We are building a product too.

Jabrod is an AI workspace for developers who would rather build their own RAG pipelines and agent workflows. It is in active development — take a look if you would sooner do it yourself than hire us.

Explore the product

Start with the audit.

Twenty minutes, no obligation, and you'll leave the call knowing roughly what your LLM spend should be — whether or not you hire us.

contact@jabrod.com