Quest 01AI
What Developers Should Know Before Building AI Tools on Professional Network Data
Some of the most practical AI projects right now are not chatbots for everything but narrow assistants: a recruiting helper that summarises a candidate's background, a sales copilot that briefs you on a prospect before a call, or a research agent that compares companies in a market. They have one thing in common. A language model alone cannot answer "who is the head of operations at this firm today?" It needs fresh, structured professional data, and how you get that data shapes the whole project.
Why the model's own knowledge is not enough
Large language models are trained on a snapshot of text. Job titles, employers and company sizes change constantly, so anything the model "remembers" about a person or firm may be months or years out of date, and it may confidently invent details that sound plausible. For tools that inform hiring or sales decisions, that is a serious flaw. The safer design is retrieval: look up current data at the moment of the request and hand it to the model as context.
Getting the data in
Developers usually weigh three routes:
- Manual exports. Fine for a prototype, useless at scale.
- Building your own scraper. Fragile, time-consuming to maintain, and likely to run into platform terms and blocking.
- A data provider. A linkedin api service such as EnvoAPI returns public profile, company and job data through REST endpoints, with SDKs in languages like Python and TypeScript and an MCP server that lets assistants call it as a tool.
For most teams the third route is the quickest way from idea to working prototype, because the hard part of collecting and normalising data is someone else's job. Read each provider's documentation on how data is sourced and what usage it allows before you commit.
Design choices that matter
Ground every answer
Pass the retrieved fields to the model in a clear, labelled block and instruct it to answer only from that block. Ask it to say "not available" rather than guess. This single prompt rule removes many hallucinated job titles.
Keep structured data structured
Do not flatten everything into a paragraph. Lists of positions with start and end dates, or company records with headcount and industry fields, let the model reason more reliably and let you show sources in the interface.
Cache sensibly
Lookups cost time and credits. Cache results for a reasonable period, but show users when the data was fetched so nobody mistakes a three-month-old record for today's.
Show your working
Users trust an assistant more when they can see which fields it used. A small "based on" panel next to each summary is cheap to build and very effective. The same idea drives good data visualisation, as covered in our piece on AI infographic generators.
Privacy is part of the spec
Professional data is still personal data. Decide early what you store, for how long and why. Avoid building features that profile people for purposes they would not expect, respect deletion requests, and check the rules in each market you serve. Treat this as a requirement from the first sprint, not a legal review at the end.
A quick pre-build checklist
- Which exact questions will the assistant answer, and which fields does each need?
- Where does each field come from, and how fresh is it?
- Does the prompt force the model to stay within the retrieved data?
- How are results cached, dated and displayed?
- What personal data is stored, and when is it deleted?
Answer those five and you will spend your time on the interesting part of the project rather than on fixing wrong answers after launch.




