AI & Automation
Practical AI and automation that ships.
AppStartDev builds AI features and automation pipelines that solve specific business problems. Document workflows, internal copilots, content processing, RAG over your knowledge base, automated reporting. The goal is reliable output your team can trust and a system you can keep improving.
Common builds
LLM-backed product features, retrieval-augmented chat over private data, document classification and extraction, internal AI assistants, intake / triage / summarization workflows, scheduled automations, report generation, data sync and integration pipelines.
Where it breaks
Where automation breaks, and how we plan around it.
Most automation looks great in a demo and falls apart on the 5% of inputs nobody anticipated. We design AI features assuming the long tail of weird real-world data is the hardest part.
The 5% of inputs you did not see.
Edge cases break automation harder than buggy code does. We test on weird, malformed, and adversarial inputs before launch.
Trust is fragile.
One wrong inference and users stop using the feature entirely. Confidence checks and human review on anything that costs real money.
Models drift, prompts rot.
What worked at launch will not work in six months. Evaluation runs in production, not only at release.
Cost per call adds up.
Caching, batching, and routing easy cases to smaller models. We track per-feature cost like any other operational expense.
How an engagement runs
Step 1
Discover
Goals, users, systems, constraints, risks.
Step 2
Shape
Release plan, design direction, scope clarity.
Step 3
Build
Focused cycles, working software, regular reviews.
Step 4
Ship & Support
Performance, security, QA, deployment, handoff.
Stacks we work with
Hire us when this is true
When you have a specific operational pain a model could solve, when you need an AI feature inside a real product (not just a demo), when you need automation across systems that currently rely on manual work, or when an existing AI feature needs to be made reliable.
FAQ
Will it hallucinate?
Some, sometimes. The job is to design the system so hallucinations are bounded, surfaced honestly, and failure modes are acceptable to the business. We use grounding, evaluation harnesses, retrieval, and review steps to keep outputs trustworthy.
Are you building agents or workflows?
Both, depending on the problem. Agents work for open-ended exploration. Workflows work for predictable, auditable processes. Most business problems we encounter are workflows with one or two model calls in them.
What about data privacy?
We design with data scope in mind. We can keep work in your tenant, use private endpoints, redact PII before model calls, or run open-weight models on infrastructure you control. The right answer depends on your sensitivity and budget.
Have an AI or automation problem we can dig into?
Tell us what you are trying to automate, what's blocking it today, and what outcome matters most.