AI product scoping
We turn “we should use AI for this” into a scoped build with a success metric, a baseline, and a kill criterion — before anyone writes a prompt.
Taking on new projects
AI, made practical.
Most AI projects die between the demo and production. We build the unglamorous parts that get them across the line — then put the same work in your users' hands as a mobile app, and teach your engineers to carry it forward.
- We build
- AI systems
- We ship
- Mobile apps
- We teach
- Your engineers
- Agent workflows
- RAG & hybrid search
- Offline & online evals
- Prompt-injection defense
- Structured output
- Model routing
- Tracing & observability
- Cost & latency budgets
- Human-in-the-loop review
- Fine-tuning & distillation
- Flutter & native mobile
- On-device inference
- App Store release engineering
- Bedrock, Foundry & Vertex AI
- LangGraph
- Claude Code
- Team workshops
What we do
Three practices, one team
They reinforce each other: the AI work informs what we put in the app, and both feed what we teach. Engage with one or all three.
01 — Build
Applied AI engineering
Agents, retrieval, evals, and guardrails — the systems that decide whether an AI feature survives contact with real users.
What we build02 — Ship
Mobile app development
Production iOS and Android apps — Flutter for one codebase, native where the platform demands it, AI built in rather than bolted on.
How we ship03 — Teach
AI training & enablement
Hands-on training for engineering teams on AWS, Azure, Google Cloud, LangGraph, Claude Code, and the practices around them.
What we teachApplied AI
Six things that decide whether AI ships
We work on the whole path — from scoping the problem to the dashboards you watch afterwards. Take the parts you need.
Agents & workflows
Tool-using systems that do real work: retries, timeouts, partial failure handling, and a human checkpoint wherever the cost of being wrong is high.
Retrieval that retrieves
Chunking strategy, hybrid keyword + vector search, reranking, and citations — measured on your questions rather than a generic benchmark.
Evals & quality gates
A graded test set, regression runs in CI, and online scoring in production — so “did that change help?” has an answer instead of a vibe.
Guardrails & safety
Input and output validation, PII handling, permission-aware retrieval, and prompt-injection defenses reviewed as part of the build, not bolted on after.
Cost & latency work
Model routing, prompt caching, batching, and streaming — quality held constant while the bill and the p95 come down.
Mobile apps
Apps that make it to both stores
We build production mobile apps end to end — design, build, release, and the monitoring after. And because we do the AI work too, the intelligent parts aren't someone else's integration problem.
One codebase with Flutter
iOS and Android from a single Dart codebase, with platform channels wherever native is genuinely the right answer.
Native where it matters
Swift and Kotlin for camera, background work, widgets, and anything the platform simply does better itself.
AI that works on a phone
Streaming responses, offline-first caching, on-device inference where it fits, and token costs that don't scale linearly with your daily actives.
Release engineering
CI builds, signing, staged rollouts, crash and performance monitoring — and the App Store and Play review process handled.
Training
We teach the stack we build on
Hands-on training for engineering teams — not slideware. Sessions are built around your codebase and your cloud, and every one ends with something running that your team wrote.
AWS
Amazon Bedrock & SageMaker
Model access through Bedrock, Knowledge Bases and agents, and the IAM, VPC, and quota plumbing that trips teams up on the way to production.
Azure
Microsoft Foundry
Building on Foundry with enterprise identity, private networking, content filtering, and the governance story your security review will ask about.
Google Cloud
Vertex AI
Vertex AI end to end — model garden, grounding, and using BigQuery as a retrieval backend instead of standing up yet another vector store.
LangGraph
Stateful agent graphs
Graphs, state, and checkpointing; human-in-the-loop interrupts and resumption; and how to debug an agent run instead of guessing at it.
Claude Code
Agentic coding in practice
Getting real leverage from Claude Code — project instructions, skills, hooks, MCP servers, subagents, and running it in CI rather than only in a terminal.
Practices
Evals & observability
The habit that makes the rest stick: building an eval harness, writing graded test sets, and tracing runs so AI work becomes reviewable like any other code.
Formats
- Half-day workshop. One topic, hands-on, laptops open the whole time.
- Multi-week cohort. Weekly sessions with exercises your team does in its own repo between them.
- Embedded enablement. We pair with your engineers while we build, and the knowledge stays behind when we leave.
- Custom track. Tell us the gap and we'll design the curriculum around it.
How we work
Four phases, and you can stop after any of them
This is how build work runs, whether it's an AI system, a mobile app, or both. A typical engagement is six to eight weeks, and each phase ends with something you own and could ship without us.
-
01
Discover
We read your data, talk to the people who will use the thing, and write down what “good” means numerically. You get a scoping doc, a graded eval set, and an honest read on whether AI is even the right tool.
Deliverable: scope + eval set
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02
Prototype
The thinnest end-to-end version that a real user can touch, scored against the eval set from day one. We find the hard failure modes here, while changing direction is still cheap.
Deliverable: working prototype + baseline scores
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03
Harden
Guardrails, retries, rate limits, tracing, cost caps, and a regression suite wired into CI. This is the phase most projects skip, and the reason most projects stall.
Deliverable: production-ready system + CI evals
-
04
Ship & hand over
Staged rollout behind flags, dashboards your team actually reads, and a walkthrough of every decision we made. The code lives in your repo on your infrastructure from week one.
Deliverable: live system + runbook + handover
Approach
Opinions we've earned the hard way
These aren't slogans — they're the rules we fall back on when a project gets difficult.
Model-agnostic, in practice
We benchmark for your task and route accordingly. Most of what we ship runs on Claude, and everything is written so swapping the model is a config change, not a rewrite.
- Claude
- TypeScript
- Python
- Flutter · Dart
- Swift · Kotlin
- Postgres · pgvector
- AWS · Azure · GCP
- OpenTelemetry
01
Measure before you argue
An eval set ends prompt debates in minutes. Without one, every opinion in the room weighs the same.
02
Boring where it counts
Postgres, queues, and plain HTTP around the model. Save the novelty for the part that needs it.
03
Smallest thing that works
A good prompt and a solid retriever beat an agent swarm more often than anyone wants to admit.
04
Design for being wrong
Models fail. What matters is whether the failure is visible, cheap, and recoverable by a person.
05
No lock-in, no black boxes
We build in your repo, on your cloud, with your API keys. When the engagement ends your team can read, run, and change everything we wrote — that's the point.
FAQ
The questions we always get
How do engagements usually start?
A short call, then a paid discovery phase — usually a week. You
come out of it with a scoping doc and an eval set you keep
regardless of whether we continue together.
We already have an AI feature that isn't working. Can you help?
That's a large share of our work. We start by building the eval
set that should have existed first, which usually turns “it
feels wrong” into two or three specific, fixable failure modes.
Can you build the mobile app as well as the AI behind it?
Yes, and it's usually the better arrangement — the two decisions
are coupled. What you stream, what you cache, and what runs on
the device all change the model and prompt design. We also take
on mobile work with no AI in it at all.
Who is the training actually for?
Working engineers — people who will be writing and reviewing this
code next week. Sessions run against your cloud accounts and
your repositories, so what your team builds during the training
is something they can keep. We're happy to run a separate
shorter session for leadership on what to expect and what to
budget for.
Who owns the code and the data?
You do, from the first commit. We work in your repository, on
your infrastructure, with your provider accounts. There is no
Aismatic runtime to depend on afterwards.
Do you work with sensitive or regulated data?
Yes — with redaction, permission-aware retrieval, retention
limits, and regional deployment designed in from the start.
Tell us your constraints early and we'll design to them.
What happens after launch?
Most teams take it from there with the runbook and dashboards we
leave behind. Some keep us on a light retainer for model
upgrades and eval reviews. Both are fine.
Contact
Tell us what you're building
A couple of paragraphs is plenty. We reply to everything, usually within one business day, and we'll tell you straight if we're not the right fit.
- No sales sequence — you'll hear from the engineers doing the work.
- Happy to sign an NDA before you share details.
- Build, ship, or train — tell us which and we'll scope it.
Business details
Aismatic LLC1552 S Route 59
Naperville, IL 60564-5941
United States
Prefer email? [email protected]
Message sent
Thanks — it's in our inbox. You'll hear back from one of the engineers, usually within one business day.