AI Product Engineering

Build the systems AI work depends on.

Agentic systems are only useful when they can process the right information, run reliably at scale, and remain reviewable. Stack55 builds those foundations and the workflows on top.

Our specialties: batch pipelines · document ingest · cloud agents · harnesses · JS/TS/Node

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Our specialties

Where Stack55 goes deep.

Five areas of engineering for large document sets, agentic jobs, and the systems that run them with control.

01

Agentic batch pipelines

Run large jobs across documents or records with queues, retries, checkpoints, and resumable work. Put review where an output needs a person.

02

Document indexing and ingest

Bring PDFs, office files, email, and other source material into a usable index. Preserve metadata, source locations, chunks, and semantic representations such as embeddings.

03

Cloud agentic systems

Coordinate agents, tools, and workers in the cloud with explicit limits for concurrency, cost, permissions, and failure. Use ADK and related runtimes where they fit the job.

04

Harnesses and sandboxes

Test prompts, tools, workflows, and failure cases in repeatable fixtures and isolated environments before an agent touches production data.

05

JS, TypeScript, and Node

Use the Vercel AI SDK and the wider JavaScript ecosystem for provider integrations, streaming interfaces, tool calls, background jobs, and maintainable services.

From source material to agentic workflow

Build the path before adding autonomy.

Each layer makes the next one more useful—and easier to inspect when something goes wrong.

01Ingest

Collect source files and records while keeping their origin and context.

02Index

Parse, chunk, enrich, and embed material so systems can find the right evidence.

03Orchestrate

Run deterministic steps, tools, and agent reasoning as a controlled workflow.

04Evaluate

Replay representative cases in a harness and sandbox, including failure paths.

05Operate

Scale cloud workloads with logs, checkpoints, permissions, and human review.

Engineering for reliable operation

A model is one component of a working system.

The surrounding software decides whether an AI capability can be trusted, changed, and used repeatedly.

01

Repeatable at volume

Batch work should resume after an interruption, show what completed, and make the remaining work clear.

02

Inspectable by design

Inputs, retrieved sources, tool calls, outputs, and review decisions should leave a trail people can examine.

03

Bounded where it matters

Permissions, budgets, and human approval belong in the system—not in a hope that the model will behave.

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See the systems in context.

Explore the products and workflow systems Stack55 is building, or bring us one process that needs a better technical foundation.