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Capability · AI Engineering

Ship AI your users actually use — without hiring an AI team.

Most startup AI never leaves the demo. We build the unglamorous 80% that makes it real: evaluation, retrieval, pipelines, monitoring — the parts between “the model answered” and “the product works.” A funded healthcare platform trusted us with their entire AI line; four months later it was live.

What we build

Concrete AI, shipped into production

Not proofs of concept that die in a notebook.

LLM features & agents
Copilots, screening agents, and automations embedded in your product — with human handoff designed in from day one, not bolted on after the first incident.
RAG & retrieval systems
Your data made answerable: embedding stores, retrieval pipelines, and grounding so the model quotes your truth instead of inventing its own.
Scoring & intelligence engines
Real-time scoring over messy operational data — the kind of engine that turns “we have records” into “we know who to call first.”
AI-adjacent plumbing
Queues, caching, SMS/MMS delivery, compliance handling, and the CI/CD that ships model changes safely — the invisible half of every AI product that works.
How we work

How AI work runs here

The difference between AI that demos and AI that ships is process, not model choice.

01

Scope against a business number

Every AI feature starts from the metric it must move — reply rate, time-to-submission, cost per screen — not from “let's add AI.”

02

Model-agnostic selection

We evaluate candidate models against your task and pick on quality, latency, and cost. When a better or cheaper model lands, we swap it — you're never locked to one vendor's pricing.

03

Evals before opinions

Test sets and evaluation harnesses come first, so “is it good enough to ship?” is a measurement, not a debate.

04

Ship continuously, monitor everything

Model changes go through the same CI/CD as code, with error tracking and observability on every call — because AI that isn't monitored is AI you'll discover is broken from a customer email.

The stack

The stack, with opinions

Every tool below has shipped production work for our clients. If it's listed, we've bled on it.

Models
Claude (Anthropic)GeminiQwen
Model-agnostic by design. Each task routes to whichever model wins on quality, latency, and cost — and when the leaderboard shifts, your product shifts with it, not your contract.
→ shipped in Wanderly CoPilot & Curate
Retrieval & data
RAG pipelinesEmbedding storesPostgreSQLMySQLMongoDB
Right store for the shape of the data. Relational where consistency matters, document where flexibility wins, embeddings where meaning is the index.
→ candidate intelligence at Wanderly
Backend & APIs
Python / DjangoNode.jsGolangGraphQL / ApolloPHP / Laravel
Python for AI logic, Go where throughput matters, GraphQL so every client speaks one language. We fit your existing stack rather than forcing a rewrite.
→ production APIs across client platforms
Infra & delivery
AWSDockerGitHub CI/CDRabbitMQRedisTwilio
Boring infrastructure, deliberately. Queues for the 60-second handoffs, caching where latency is felt, and pipelines that make Friday deploys unremarkable.
→ Wanderly's AI ships continuously
Reliability & security
SentryCloudWatchJWT auth
If it isn't monitored, it's broken and you don't know yet. Error tracking, observability, and token-based auth on every service we ship.
→ standard on every engagement
Frontends
Next.jsFlutterReact NativeNative iOS / Android
Next.js for the web your investors see; Flutter when one codebase must hit both stores fast. Native when the product demands it — and we'll tell you when it doesn't.
→ Petx apps: 3 engineers, 4 months, both stores
Proof, not promises

Wanderly: a full AI product line in 4 months

What this capability looks like when it ships.

Funded scale-up · Healthcare staffing

Wanderly needed to move into AI with no AI team of their own. Three engineers and a product owner shipped their entire AI capability: CoPilot, an AI recruiter that screens candidates, collects documents, and hands off to a human inside 60 seconds — and Curate, a healthcare-native engine scoring candidate readiness in real time.

3 engineers + 1 PO4 months4.2× faster to submission-ready92% reply rate on top leads
“Working with the team was a dream run. We finished every assignment on time and delivered.”Zia · Wanderly
Straight answers

Straight answers on AI projects

“Demo or production?” Production.

Evals, monitoring, and human handoff are scoped from sprint one. If a feature can't be measured, we don't call it done.

“Which model should we use?” The one that wins your eval.

We test candidates against your actual task and data. Vendor loyalty is your cost problem, not your quality strategy.

“What about our data?” Yours, always.

Your data stays in your cloud, IP transfers fully, and NDAs come before the first call. Compliance-heavy domains are where we've shipped.

Start with a Blueprint, not a leap.

The $5,000 Decipher Blueprint works for AI features too: fourteen days in, you hold a working concept, an architecture, and an honest quote — fully credited if you build with us.