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// r7-001 · system online · est. 2024

Applied AI, engineered for production.

r7labs is an applied AI lab. We research, develop, and deploy intelligent systems for teams across healthcare, finance, security, and beyond — AI that ships into real workflows, not slideware.

Start a project What we do
12+
AI systems in production
8
verticals served
0
demo-ware shipped
LLM Agents● Retrieval (RAG)● Fine-tuning● Evaluations● Computer Vision● Speech & Audio● Forecasting● Document AI● Multimodal● MLOps● LLM Agents● Retrieval (RAG)● Fine-tuning● Evaluations● Computer Vision● Speech & Audio● Forecasting● Document AI● Multimodal● MLOps●
[ 01 ] services

Three ways we partner with your team.

Most engagements blend all three. We mix and match — strategy when you need direction, implementation when you need to ship, training when you need your team to own it long-term.

01 — consulting

AI Consulting

Strategy from people who actually ship.

Most AI advice comes from firms that have never put a model into production. We help leaders pick the right problems, the right approaches, and avoid the expensive detours — grounded in what actually works at the seams.

  • Strategy & roadmap
  • Use-case discovery & prioritization
  • Build vs. buy analysis
  • Model & vendor selection
  • Risk, governance & compliance review
  • ROI modeling & business case
For leaders making first or next-stage AI bets.
02 — implementation

AI Implementation

From prototype to production.

We build the systems. Agents, retrieval pipelines, fine-tuned models, and multimodal stacks — engineered for the parts of production that demos never show: latency, cost, reliability, and the failure modes you only learn about at 3am.

  • LLM agents & tool-use systems
  • Retrieval-augmented generation (RAG)
  • Fine-tuning & custom models
  • Multimodal pipelines — vision, speech, document AI
  • Evals, guardrails & observability
  • Integration into your existing stack
For teams who need a system, not another demo.
03 — training

AI Training

Upskill your team on the real stack.

Most AI training is slideware. We coach engineering, product, and leadership teams on the same techniques we use in production — hands-on labs and real code, with a curriculum tailored to your stack and your goals.

  • Hands-on workshops (1–3 days)
  • Embedded coaching & pair programming
  • Executive briefings for leadership
  • Custom curriculum for your stack
  • Eval-driven exercises on real data
  • Ongoing office hours & reviews
For organizations that want to own this long-term.
[ 02 ] engagements

What teams actually hire us for.

Concrete engagement shapes drawn from real client work — across our consulting, implementation, and training services. Most start with one of these and grow from there.

E.01 — strategy

AI strategy & opportunity mapping

Where to point the spend.

Most AI roadmaps are wishlists in a deck. We work alongside leadership to map the real leverage points — where AI moves a metric, where it doesn't, and where today's frontier still falls short — and produce a plan you can defend to a board.

  • Opportunity audit & scoring
  • Use-case prioritization workshops
  • Executive briefings on the current frontier
  • Eval & success-metric design
  • Reference architectures and build/buy guidance
Often the first engagement. 2–4 weeks.
E.02 — due diligence

Technical due diligence

Real systems, hyped demos, and how to tell them apart.

Investors, acquirers, and finance teams ask us to independently assess AI claims — distinguishing a polished prototype from a system that can actually carry a business. We pull on the architecture, the data, the evals, and the moat.

  • Architecture & codebase review
  • Data quality, coverage & licensing review
  • Eval replication and benchmark stress-tests
  • Cost, latency & scaling analysis
  • Defensibility & moat assessment
1–3 week sprint. Defensible written report.
E.03 — workflow design

Human–AI workflow design

What AI does, what humans do, and what neither should touch.

The hardest part of deploying AI isn't the model — it's the workflow around it. We map your real processes, identify where AI adds leverage and where it adds risk, and design human-in-the-loop systems operators actually want to use.

  • Process & task-level mapping
  • Human-in-the-loop interaction design
  • Trust calibration & escalation logic
  • Operator enablement & change management
  • Productivity baselines & measurement
Pairs with any implementation engagement.
E.04 — data systems

Data & decision systems

Get more signal out of the data you already have.

Before you fine-tune a model, get clear on what the data is telling you. We build analytics pipelines, decision-support models, and behavioral analyses that surface what matters — sometimes solving the problem outright, sometimes setting up a much cheaper AI system later.

  • Analytics & reporting pipelines
  • Decision-support modeling
  • Behavioral & cohort analysis
  • Targeted study & experiment design
  • Workflow instrumentation
Often surfaces wins that don't need an LLM at all.
[ 03 ] principles

How we think about applied AI.

  1. P.01

    Eval-driven

    We measure what works. No vibes, no demo theater. Every system ships with a test set we can defend in a room full of skeptics.

  2. P.02

    Production-first

    If it doesn't survive a Tuesday at 3pm, it doesn't ship. We design for latency, cost, and reliability from day one — not as a phase-two retrofit.

  3. P.03

    Embedded

    We work as part of your team, not a vendor at arm's length. Same Slack, same standups, same on-call. Knowledge stays with you when we leave.

  4. P.04

    Vendor-agnostic

    We pick the model that fits your problem, not the one with the best margin or the loudest CEO. Frontier, open, or fine-tuned — whatever earns its keep.

[ 04 ] process

From eval set to production. Then we keep training.

  1. Phase 01

    Discover

    One week. We map the workflow, find the leverage point, and scope the smallest experiment that can prove the thesis.

  2. Phase 02

    Prototype

    Two weeks. Pick the model, build the eval set, ship a working slice end-to-end. Bring data, not slides.

  3. Phase 03

    Productionize

    Latency, cost, reliability, guardrails. The boring engineering that turns a notebook demo into a dependable system.

  4. Phase 04

    Operate

    Continuous evals, drift monitoring, retraining loops. Most clients keep us as their AI team for the long haul.

[ 05 ] training programs

We also teach what we build.

Most AI training is slideware. We coach engineering, product, and leadership teams on the same techniques we use in production — agents, retrieval, evals, fine-tuning — with hands-on labs and real code.

01

Workshops

One- to three-day intensives for engineering and product teams. Pick the modules: agent design, retrieval systems, evals, prompt engineering, fine-tuning. Hands-on from hour one.

  • Agents
  • RAG
  • Evals
  • Fine-tuning
02

Embedded coaching

A senior practitioner pairs with your team week over week. Code reviews, design sessions, and pair programming on the hard parts of shipping AI into production.

  • Pairing
  • Code review
  • Mentorship
  • Design
03

Executive briefings

Half-day sessions for leadership teams. Where AI actually creates leverage, where it doesn't, what to invest in this quarter, and what to skip entirely.

  • Strategy
  • ROI
  • Roadmaps
  • Risk
[ 06 ] let's build

Have an AI initiative
that needs to ship?

We take on a small number of engagements each quarter — across healthcare, finance, security, and beyond. If your initiative sounds like a fit, drop us a line.

support@r7labs.io