The demo is the easy fifth
A prompt in a notebook proves nothing about cost, latency, failure modes, or what happens at 3am. We build the other four-fifths: queues, retries, evals, observability, rollback.
EQUALSINTERACTIVE
Scope a project
Agentic AI Engineering — Chicago
It’s not artificial, it’s interactive.
Equals Interactive builds software and interactive products — and the autonomous AI systems that increasingly run inside: agent swarms, automated code review, retrieval across your own data.
A decade of cloud-native platform engineering sits behind every build. Some of it is agent work; much of it is simply software that has to run.
01 / Position
Most AI work stops at the demo.
Autonomy needs a gate
Agents that can act need hard-coded guardrails, an automated review pass, and a human on the escalation path. Autonomy is a permission you grant in increments, not a switch.
Model choice is a config value
Mixed local and hosted models behind one gateway. Route by cost, sensitivity, or capability — and swap what's underneath without rewriting the product on top of it.
02 / Capabilities
Three things, done properly.
Deep in agentic systems and the data underneath them. The rest of the stack is in service of those.
01
Agent systems & automated code review
Multi-agent swarms that plan, build, test and critique against a defined task graph — with guardrails compiled in and a human on call for anything the gate won't pass.
- ▸Agent orchestration and task decomposition
- ▸Automated open code review in CI
- ▸Hard-coded guardrails and policy gates
- ▸Human-in-the-loop escalation
- ▸Model routing for cost and sensitivity
- ▸Evaluation harnesses and regression suites
02
Retrieval over your own data
Vector search finds the passage; a graph explains how it connects. We build both, so answers survive questions that span documents, entities and time.
- ▸Vector and graph retrieval (RAG) pipelines
- ▸Ingestion, chunking and embedding strategy
- ▸Semantic association and entity linking
- ▸Trend and news intelligence workflows
- ▸Document processing and extraction
- ▸Grounding, citation and answer auditing
03
The product around it
An agent is a backend service. It still needs an interface, auth, tenancy, billing and a deploy pipeline. Full-stack product engineering, built by the same people.
- ▸React and Next.js application development
- ▸Node.js, Python and Go services and APIs
- ▸Multi-tenant SaaS architecture
- ▸Real-time and streaming interfaces
- ▸Speech-to-speech and audio pipelines
- ▸Cloud infrastructure, CI/CD, observability
04 / Open door
And the work that isn't AI at all
Plenty of good problems don't need a model. Legacy modernization, a cloud migration, an integration nobody wants to own, an internal tool that saves a team a day a week — ten years of engineering leadership applies just as well. If it's interesting and it ships, bring it.
03 / Our apps
We ship our own, too.
The same stack, pointed at our own products — where we find out what actually holds up.
Rmndr
Beta
Never forget what matters.
Write a reminder the way you'd say it out loud — in English, Spanish, French, Mandarin, Arabic, Hindi and more. Rmndr reads the sentence, works out when you meant, and reaches you by email, SMS or a lifelike AI voice call.
Natural language
Email · SMS · AI voice
Release-aware dates
Private by default
Open rmndr.me ↗
Fig. 02 — Plain language inParsed out
“Review Q2 budget report”
Today 3pm
“Call dentist for appointment”
Mon 10am
“Invoice Acme on the 1st”
1st monthly · 9am
“When Dune Part 3 hits theaters”
Release day
04 / Engagements
Four ways this usually starts.
01
Most requested
Fractional CTO
You're funded, you have a roadmap, and you don't have an engineering organization yet. We set architecture, hire against it, and build the first version while you do.
02
Scoped build
A defined problem, a budget and a date. Discovery and architecture, then milestones you can actually check — and a handover your own team can maintain.
03
Sovereign deployment
Your data cannot leave your network. Open-weight models served on your own hardware or private cloud, routed through a gateway you control and audit.
04
White label
You're the agency and the client relationship is yours. We're the AI engineering team behind it, under your name, in your process, on your calls if you want us there.
05 / Stack
The parts, in plain English.
Six moving parts. You don’t need to know the names underneath — but everything listed is running in something we’ve shipped.
The workers
Software that takes a job, splits it into steps, does them, and checks its own work before a person ever sees it. This is the part people mean when they say “AI agent.”
Agent swarms · task graphs · tool calling · automated code review · human-in-the-loop gates · evaluation harnesses
The brains
The AI models themselves, and where they live — on your machines, ours, or a vendor’s. Trading one for a cheaper or smarter model is a settings change, not a rebuild.
Claude · Ollama · vLLM · llama.cpp · LiteLLM routing · Hugging Face endpoints · fine-tuning · PyTorch · TensorFlow
The memory
How the system looks things up in your own documents, contracts and records — so answers come from your business, with a source you can check, instead of from the open internet.
PostgreSQL · pgvector · graph retrieval · OpenSearch · Elasticsearch · Redis · GraphQL · JupyterLab
The thing people use
The website, app or dashboard your staff and customers actually click on. Everything above is invisible until this part is good.
TypeScript · Python · Go · React · Next.js · Node.js · FastAPI · Django · Angular
The phone line
Systems that listen and answer out loud in real time, so a caller can hold a conversation instead of pressing 1 for sales.
Speech-to-speech pipelines · ASR · real-time audio streaming · telephony notification · FFmpeg
The plumbing
Where all of it runs, how updates ship without taking the business down, and what pages someone at 3am when it breaks.
AWS · GCP · Kubernetes · Terraform · Docker · NGINX · Caddy · GitHub Actions · GitLab CI/CD · Turborepo
06 / Questions
Asked before the first call.
What is an AI agent system, and how is it different from a chatbot?
A chatbot answers. An agent system decides and acts — it breaks a goal into tasks, calls tools and APIs, checks its own output, and escalates what it can't resolve. The engineering is in the checking and the escalation, not the answering.
Can the models run on our own infrastructure?
Yes. Open-weight models served with Ollama, vLLM or llama.cpp on your hardware or private cloud, routed through LiteLLM so cost, sensitivity and fallback rules live in one auditable place. Mixed deployments — local for sensitive data, hosted for the rest — are the common shape.
How do you stop an autonomous agent doing something destructive?
Guardrails compiled into the harness rather than written into a prompt, least-privilege tool access, an automated review pass before anything merges or sends, and a human notified on every escalation path. Permissions widen as the evals earn it.
Why vector and graph retrieval instead of just embeddings?
Vector search is good at "find me something similar" and bad at "how are these two things related". Questions that cross documents, entities or time need structure. We build both and let the query decide which one answers it.
Do you take work that has nothing to do with AI?
Regularly. Web applications, SaaS platforms, API and integration work, cloud migration, legacy modernization, internal tools. The AI focus is where the depth is; it isn't a filter on the work.
How does an engagement start?
Send the scoping form below. We read it, then set up a call to pressure-test the idea. If there's a fit, you get a written architecture and a milestone plan before anyone signs anything.
07 / Scope
Scope a
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project.
Tell us what you're trying to build and what's in the way. Every inquiry gets a real answer from an engineer — not a calendar link.
BasedChicago, Illinois — all US time zones
NDASigned before discovery, on request
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