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Feature Research takes the features identified in your market research and examines them in depth. Rather than giving you a surface-level list of what competitors offer, it goes a level deeper: how do they implement each feature, what patterns are common, what technical approaches do they use, where are the edge cases, and what open-source options exist. The pipeline finishes with a prioritized build blueprint and an MVP day estimate, giving you a direct path from research to planning.

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How the pipeline works

Feature Research runs four stages sequentially:
StageWhat happens
scopeThe AI determines which competitors to analyze for each feature
gatherPages, docs, and product content are fetched for each competitor
analyzeEach feature–competitor pair is analyzed in depth
synthesizeA cross-competitor blueprint and build order are generated

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Starting a feature research job

1

Identify features to analyze

You need a list of features — either from a completed market research job or entered manually. Each feature needs an id, a name, and a source.
2

Submit the job

Send a POST /feature-research request. The endpoint returns immediately with a job ID.
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features
array
required
List of features to analyze. Each item must include id (string), name (string), and source (string, e.g. "research").
​
idea
string
required
Your product idea, up to 5,000 characters. Provides context for competitive scoping.
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project_id
string
UUID of a project to attach results to. Pass null to leave unattached.
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research_id
string
UUID of a completed market research job. When provided, the pipeline uses that research as additional context during the scope stage.
curl --request POST \
  --url https://api.manticscore.com/feature-research \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
    "features": [
      {"id": "feat_001", "name": "Automated invoice generation", "source": "research"},
      {"id": "feat_002", "name": "Time tracking with project tagging", "source": "research"}
    ],
    "idea": "A mobile app that helps freelancers track time and automatically generate invoices",
    "project_id": "proj_uuid_here",
    "research_id": "research_uuid_here"
  }'
Response 202:
{
  "job_id": "b2c3d4e5-...",
  "status": "queued"
}
3

Stream or poll for results

Stream progress via the events endpoint, or poll the status endpoint until status is completed.

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Streaming progress events

curl
curl --request GET \
  --url 'https://api.manticscore.com/feature-research/b2c3d4e5-.../events?cursor=0' \
  --header 'Authorization: Bearer <token>'
The event stream follows the NDJSON v1 protocol. Key events:
EventDescription
stageA pipeline stage started, completed, or failed
progressFree-text status message within a stage
feature_analyzedEmitted each time one feature finishes analysis
resultFull results payload, emitted on completion
errorNon-retryable failure
doneStream closed
If you reconnect after a disconnect, pass cursor=<last_seq> to replay missed events from where you left off. If the job is already complete and you connect with cursor=0, the full result is replayed as a single result event.

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Fetching completed results

Once the job status is completed, fetch the full analysis: 
curl --request GET \
  --url https://api.manticscore.com/feature-research/b2c3d4e5-.../results \
  --header 'Authorization: Bearer <token>'

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Result shape

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analyses
array
One entry per feature. Each entry contains:
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blueprint
object
A cross-feature build plan synthesized from all analyses.
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quality
object

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Auto-chaining from market research

You don’t need to start feature research manually. When you run POST /research with mode=feature, ManticScore automatically chains into feature deep research on the top 5 features once market research completes.
curl
curl --request POST \
  --url https://api.manticscore.com/research \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
    "idea": "A mobile app that helps freelancers track time and automatically generate invoices",
    "mode": "feature"
  }'
The response includes "chain_feature_research": true, and you’ll receive a push notification when the feature analysis completes.

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Limits and credits

Value
Rate limit5 requests / minute
Credit cost3 credits per job
Max idea length5,000 characters
GET /feature-research/{job_id}/results returns a 409 if the job has not yet completed. Check the status endpoint or wait for the result stream event before fetching results.