Deep-dive how competitors implement specific features

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.

How the pipeline works

Feature Research runs four stages sequentially:

Stage	What happens
scope	The AI determines which competitors to analyze for each feature
gather	Pages, docs, and product content are fetched for each competitor
analyze	Each feature–competitor pair is analyzed in depth
synthesize	A cross-competitor blueprint and build order are generated

Starting a feature research job

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.

Submit the job

Send a POST /feature-research request. The endpoint returns immediately with a job ID.

array

required

List of features to analyze. Each item must include id (string), name (string), and source (string, e.g. "research").

string

required

Your product idea, up to 5,000 characters. Provides context for competitive scoping.

string

UUID of a project to attach results to. Pass null to leave unattached.

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"
  }'

import requests

response = requests.post(
    "https://api.manticscore.com/feature-research",
    headers={"Authorization": "Bearer <token>"},
    json={
        "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",
    },
)
print(response.json())

Response 202:

{
  "job_id": "b2c3d4e5-...",
  "status": "queued"
}

Stream or poll for results

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

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:

Event	Description
`stage`	A pipeline stage started, completed, or failed
`progress`	Free-text status message within a stage
`feature_analyzed`	Emitted each time one feature finishes analysis
`result`	Full results payload, emitted on completion
`error`	Non-retryable failure
`done`	Stream 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.

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>'

import requests

response = requests.get(
    "https://api.manticscore.com/feature-research/b2c3d4e5-.../results",
    headers={"Authorization": "Bearer <token>"},
)
print(response.json())

Result shape

array

One entry per feature. Each entry contains:

Show properties

string

The feature ID you submitted.

array

How each competitor implements this feature, including UX patterns and notable details.

array

Patterns that appear across multiple competitors — your baseline table stakes.

array

Technical strategies used, from simple heuristics to ML-based approaches.

array

Known edge cases and how different products handle them.

array

Open-source libraries and tools relevant to building this feature.

object

A cross-feature build plan synthesized from all analyses.

Show properties

array

Prioritized feature recommendations with rationale.

array

Suggested implementation sequence based on dependencies and impact.

number

Estimated total days to reach a shippable MVP across all analyzed features.

string

A concise narrative summary of the overall build strategy.

object

Show properties

number

Overall quality score for this analysis, from 0 to 10.

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.

Limits and credits

	Value
Rate limit	5 requests / minute
Credit cost	3 credits per job
Max idea length	5,000 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.

Get Started

Core Features

Integrations

Real-Time Streaming

Account & Settings

Deep-dive how competitors implement specific features

How the pipeline works

Starting a feature research job

Streaming progress events

Fetching completed results

Result shape

Auto-chaining from market research

Limits and credits

​How the pipeline works

​Starting a feature research job

​Streaming progress events

​Fetching completed results

​Result shape

​Auto-chaining from market research

​Limits and credits

How the pipeline works

Starting a feature research job

Streaming progress events

Fetching completed results

Result shape

Auto-chaining from market research

Limits and credits