Parallel development at ZeroPixel
Search engines are built for humans: ten results, a page each, and a person to read them. That's the wrong shape when the thing doing the searching is your software. Parallel is a research API designed for the other case — you describe what you need to know, it does the reading across the open web, and it returns a structured answer with the sources it came from.
The citations are the part that makes it usable in a business context. An AI feature that produces a confident paragraph from nowhere is a liability; one that produces the same paragraph with the four pages it came from is something your team can check, and something you can defend when a customer asks where a claim came from.
We integrate it where a product needs current information rather than whatever a model memorised during training: enriching leads and company records, monitoring competitors and prices, doing due diligence at volume, and answering user questions with sources attached. The engineering work is caching, cost control and validation — because a research call is far more expensive than a database query, and treating it like one is how AI budgets get out of hand.
What we build with Parallel
Four shapes, all of them replacing a person opening twenty tabs to answer a question that recurs every week.
Research & enrichment pipelines
Taking a list of companies, people or products and returning a structured, sourced profile for each — at a volume no one could do manually.
Answers with sources
Product features that answer questions from the live web with citations attached, so users and your team can verify rather than trust blindly.
Market & competitor monitoring
Scheduled research runs that watch prices, competitors, tenders or regulation, and tell you what changed rather than everything they found.
Record enrichment & verification
Filling the gaps in your CRM or database and checking existing records against the live web — with confidence scores and the source for each field.
Why hire ZeroPixel for Parallel work
- Cost control is part of the build
- Research calls are orders of magnitude more expensive than the queries developers are used to. We cache aggressively, batch sensibly and set hard budget ceilings — so the feature that looked cheap in testing doesn't surprise you at scale.
- Verification, not vibes
- Every enriched field arrives with a source and a confidence level, and anything below the threshold gets flagged for a human rather than written silently into your database. Bad data that looks confident is worse than a blank field.
- We've built the data side
- We've shipped products that mine and enrich millions of UK records. Research APIs are one input into that kind of pipeline — the rest is deduplication, validation and storage, which is where these projects usually go wrong.
Parallel — common questions
How is this different from just calling ChatGPT?
A general model answers from what it learned during training, which is frozen at a date and can't be checked. A research API goes and reads the live web for each request, then returns an answer with the pages it used. For anything where being current and being verifiable matter — pricing, company data, regulation, competitors — that difference is the whole point.
Can we trust the results?
Trust the sources, not the summary. That's why we build every integration to surface citations and confidence scores rather than a bare answer, and to route low-confidence results to a person. Used that way it's genuinely reliable; used as an oracle that's never wrong, any AI system will eventually embarrass you.
What does it cost to run?
Per request, and considerably more than a normal API call, because real research is happening behind each one. The engineering answer is caching and batching — most businesses ask overlapping questions, so a well-built pipeline reuses far more than it fetches. We model your expected volume and give you a monthly running-cost figure before you commit to the build.
Can it enrich our existing CRM data?
Yes, and it's one of the most common requests. We take an export or connect directly, enrich the records in batches, apply validation rules, and write back only the fields that pass. You get a report of what changed, what was uncertain and what needs a human — rather than an unreviewable overwrite of your customer database.
How much does an integration cost?
A single enrichment or research pipeline is typically a low-to-mid four-figure project including caching, validation and monitoring. Product features with a user-facing research experience are quoted on scope. Fixed written quote before we start, with the platform's own usage costs set out separately and honestly.
Beyond Parallel
A stack is a set of choices that have to work together — these are the pieces we most often pair with Parallel, and where we do it.
Building with Parallel?
Tell us what you're making — we'll reply within one working day with an honest take and a fixed quote.