LaunchPad Lab — AI Solutions
AI development services for real products
Most AI projects fail for reasons that have nothing to do with the model. The data isn't connected, nobody defined what a correct answer looks like, or the feature shipped as a demo and never reached a workflow people actually use.
Where AI features earn their place
The clearest wins arrive in three shapes of work. Summarization and extraction, where documents or tickets get read and turned into structured fields. Retrieval, where someone needs an answer pulled from a large internal knowledge base. And orchestration, where a model routes, drafts, or classifies inside a process that already exists.
If your use case doesn't fit one of those shapes, it's worth pressure-testing before committing budget. We'll say so during scoping rather than after a build.
Three shapes of work
- ReadSummarization and extraction over documents and tickets
- FindRetrieval across your own internal knowledge base
- RouteOrchestration inside a workflow your team already runs
LLM-powered applications
LLM-powered applications, not chatbots bolted on
An LLM-powered application treats the model as one component in a larger system. That means defining inputs and outputs the software can validate, handling failures and timeouts, and logging what the model returned so quality can be reviewed later. It also means choosing a model per task instead of defaulting to whichever one is loudest in the news.
Claude 3.5 Sonnet performs well on writing, coding, and reasoning tasks inside product workflows. OpenAI GPT-5 is a common default for general-purpose features. The choice comes out of evaluation on your data, not benchmark rankings.
Writing and reasoning
Claude 3.5 Sonnet is often the evaluation winner for drafting, code assistance, and multi-step reasoning inside a product surface.
General-purpose default
OpenAI GPT-5 is a common starting point for broad features such as classification, summarization, and draft generation.
RAG implementation over your own documents
Retrieval-augmented generation is how you keep answers grounded in company data instead of the open internet. The work is mostly data engineering: chunking documents sensibly, keeping an index current as sources change, and attaching citations so users can check the source. Poor chunking is the most common reason a RAG system looks good in a demo and fails in daily use.
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Chunk the documents
Split sources by real document structure — sections, clauses, tables — instead of fixed character windows. Chunking choices decide what the retrieval step can ever find.
Retrieval quality
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Keep the index current
Sources change, get replaced, and retire. We build the refresh path up front so the index doesn't quietly drift away from what the business actually knows.
Index upkeep
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Attach citations
Every answer points back to the passage it came from so a user can confirm it in one click. Citations are what make a retrieval answer trustworthy enough to act on.
Verifiable answers
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Pick a long-context model when it fits
Gemini 1.5 Pro is often a fit when documents are long and context-heavy, since long-context processing is one of its strengths. We still test it against the shorter-context setup rather than assuming the answer.
Model fit
Ask about RAG implementation before you commit to a vendor or a stack — the data work is where the schedule lives.
Ask about RAG implementationAI copilot development
AI copilot development inside existing products
A copilot is different from a chatbot. It sits inside a workflow, sees the context the user is working in, and proposes a next action rather than waiting for a prompt. Building one means deciding what the copilot can see, what it can change, and where a human confirms before anything is committed.
In practice the confirmation step is what makes teams comfortable turning it on for real accounts. It also means the copilot has to fail gracefully — proposing nothing is a better outcome than proposing something wrong that ships silently.
What it can see
The records, threads, and fields open in the user's current screen — scoped so nothing leaks across accounts or roles.
What it can change
Drafts and suggestions land in place; commits go through a person, so a stray model output never becomes a stray database write.
Workflow automation
Workflow automation with a model in the loop
Automation gets more useful when a model handles the judgment calls that used to break rule-based systems. Document triage, ticket routing, and draft generation are common examples. The design principle we hold to: the model proposes, the system validates, and exceptions route to a person. That keeps a bad model output from becoming a bad business outcome, and it gives you a log of where human review was needed.
Accuracy, grounding, and knowing when the answer is bad
Every AI feature needs an evaluation set: real questions with known correct answers, run against the system whenever the prompt, model, or data changes. It's unglamorous and it's the difference between a feature you can improve and one you can only hope about.
Refusal behavior
We build in refusal behavior, so the assistant says it doesn't know rather than inventing an answer. A confident wrong answer is worse than a blank one.
Source citations
Users can verify what they're told. Citations turn a plausible answer into a checkable one, and they show you which sources the system actually trusts.
What a typical AI engagement looks like
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Scoping
One to two weeks. We define the use case, review your data sources, and pick an evaluation approach before any build begins.
Step 1
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Build
A build phase adds retrieval, the interface, and the evaluation harness, usually alongside the product it lives in rather than as a separate app.
Step 2
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Review and tune
After launch we review failed queries and tune chunking, prompts, or model choice. If your data isn't ready, we'll tell you that first, because no model choice fixes an unusable knowledge base.
Step 3
Straight answers
Where AI doesn't fit yet
Some requests should stay conventional software. Deterministic calculations, compliance rules, and anything where an error is unacceptable are better handled by ordinary code. If a task needs exact reproducibility, a model adds risk without adding much.
We've turned down AI work where a scheduled script or a better database query solved the problem for a fraction of the cost, and we'd rather do that than sell a feature you don't need. If the honest answer is software without a model, that's what we'll propose.
Questions we hear before an AI build starts
Do I need a chatbot, or a copilot inside the product?
If people already work inside your app and the friction is "I have to go find the answer," a copilot nested in the workflow usually wins. A standalone chat interface makes sense when the main job is asking questions across a broad knowledge base and there's no obvious screen to embed into.
How long does a retrieval-augmented generation build take?
Scoping runs one to two weeks. The build that follows depends mostly on the health of your documents and sources — clean, well-structured material moves fast; scattered files and stale copies add weeks. We flag that during scoping so nobody is surprised later.
Which model should we use, and can we change our mind?
We choose per task and verify on your evaluation set. Model choice is deliberately a configuration decision rather than something welded into the product, so swapping Claude 3.5 Sonnet, OpenAI GPT-5, or another option later means re-running the evaluation instead of rebuilding the feature.
Can you start if our data is a mess?
Yes, but the first slice of work becomes data preparation. We'd rather spend the intake on cleaning and structuring sources than build retrieval on top of material nobody trusts. If that prep is a dealbreaker, it's better to know at scoping than halfway through a build.
Where do you work from, and who do we talk to?
LaunchPad Lab is an IT company specializing in web, mobile, software development, and AI solutions. We're based at 448 N La Salle Dr Fl 9, Chicago, IL 60654, United States, reachable Monday–Friday, 9:00 AM – 6:00 PM at +1 415-526-3479 or [email protected]. Most AI engagements run remotely with scheduled check-ins.
Tell us the workflow you want AI to help with
Send the use case in plain language — what people do today, where it slows down, and which system holds the data. We'll come back with a scoping view: whether a model belongs in it, what to evaluate, and what the build would include. If a simpler software change solves it, we'll say that too.
Phone
+1 415-526-3479
Email
[email protected]
Studio
448 N La Salle Dr Fl 9, Chicago, IL 60654, United States
Hours
Monday–Friday, 9:00 AM – 6:00 PM