Feature deep research API — multi-competitor analysis

Feature deep research runs a four-stage pipeline — scope, gather, analyze, synthesize — that examines how competitors have implemented specific features. For each feature you pass in, the pipeline identifies which competitors have built it, scrapes their implementations, analyzes patterns and edge cases, and produces a blueprint with recommended build order and a risk matrix. You can start a job manually or let it auto-chain from a completed mode=feature market research run.

Start feature deep research

Rate limit: 5 requests per minute.

object[]

required

The features to analyze. Each object must have an id, a name, and a source.

Show feature object properties

string

required

A stable identifier for the feature, such as the ID from a research result.

string

required

Human-readable feature name, e.g. "OCR receipt scanning".

string

required

Where the feature came from. Use "research" for features pulled from a ManticScore research run.

string

required

The product idea that provides context for the analysis. Maximum 5,000 characters.

string

UUID of the project to associate this job with. Pass null for unattached jobs.

string

UUID of the market research run that produced the features. Providing this gives the pipeline additional context.

curl -X POST https://api.manticscore.com/feature-research \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "features": [
      {"id": "feat_001", "name": "OCR receipt scanning", "source": "research"},
      {"id": "feat_002", "name": "Bank sync", "source": "research"}
    ],
    "idea": "AI expense tracker for freelancers",
    "project_id": "9f4e2a1b-...",
    "research_id": "7c3d1e9a-..."
  }'

import httpx

resp = httpx.post(
    "https://api.manticscore.com/feature-research",
    headers={"Authorization": "Bearer <token>"},
    json={
        "features": [
            {"id": "feat_001", "name": "OCR receipt scanning", "source": "research"},
            {"id": "feat_002", "name": "Bank sync", "source": "research"},
        ],
        "idea": "AI expense tracker for freelancers",
        "project_id": "9f4e2a1b-...",
        "research_id": "7c3d1e9a-...",
    },
)
print(resp.json())

202 response

{
  "job_id": "d2e7b4c1-...",
  "status": "queued"
}

Stream progress from GET /feature-research/{job_id}/events. Poll status from GET /feature-research/{job_id}/status.

Get job metadata

Returns lightweight metadata about a feature research job. Safe to poll while the job is running.

curl "https://api.manticscore.com/feature-research/d2e7b4c1-..." \
  -H "Authorization: Bearer <token>"

200 response

{
  "id": "d2e7b4c1-...",
  "status": "running",
  "pipeline_stage": "analyze",
  "selected_features": [
    {"id": "feat_001", "name": "OCR receipt scanning"}
  ],
  "quality": {
    "overall_score": 7.5
  },
  "error_detail": null,
  "created_at": "2026-04-18T16:00:00Z",
  "updated_at": "2026-04-18T16:20:00Z"
}

string

required

One of queued, running, completed, failed.

string

required

Current stage in the pipeline: queued, scope, gather, analyze, synthesize, completed.

object[]

The features the pipeline is processing, with their resolved IDs and names.

object

Show properties

number

Quality score on a 0–10 scale. Only meaningful once the job is completed.

string

Present only on failed status. Human-readable description of what went wrong.

Get job status (lightweight)

Returns only the status fields without the feature list. Suitable for frequent polling.

curl "https://api.manticscore.com/feature-research/d2e7b4c1-.../status" \
  -H "Authorization: Bearer <token>"

200 response

{
  "status": "analyze",
  "last_event_seq": 12,
  "pipeline_stage": "analyze"
}

Use last_event_seq as the cursor when reconnecting to the events stream.

Get full results

Returns the complete analysis output. Only available once the job has status: completed.

curl "https://api.manticscore.com/feature-research/d2e7b4c1-.../results" \
  -H "Authorization: Bearer <token>"

import httpx

resp = httpx.get(
    "https://api.manticscore.com/feature-research/d2e7b4c1-.../results",
    headers={"Authorization": "Bearer <token>"},
)
data = resp.json()
print(data["blueprint"]["strategic_summary"])

200 response

{
  "id": "d2e7b4c1-...",
  "status": "completed",
  "selected_features": [
    {"id": "feat_001", "name": "OCR receipt scanning"}
  ],
  "scope": {
    "features": [
      {
        "id": "feat_001",
        "competitors": ["Expensify", "Receipts by Wave"]
      }
    ]
  },
  "analyses": [
    {
      "feature_id": "feat_001",
      "implementations": [],
      "common_patterns": [],
      "technical_approaches": [],
      "edge_cases": [],
      "oss_landscape": []
    }
  ],
  "blueprint": {
    "recommendations": [],
    "build_order": [],
    "risk_matrix": [],
    "total_mvp_days": 15,
    "strategic_summary": "OCR receipt scanning is the highest-leverage feature..."
  },
  "sources": [
    {
      "id": "src_001",
      "url": "https://use.expensify.com/receipt-scanning",
      "title": "Expensify SmartScan"
    }
  ],
  "quality": {
    "overall_score": 7.5,
    "dimensions": {}
  }
}

object

The competitor set the pipeline identified for each feature during the scope stage.

object[]

Per-feature analysis. Each entry contains implementations found in the wild, common design patterns, technical approaches, edge cases discovered, and the open-source landscape.

object

Show properties

array

Prioritized list of implementation recommendations.

array

Suggested order to implement the features, accounting for dependencies.

array

Identified risks per feature with severity ratings.

number

Estimated total days to reach an MVP across all analyzed features.

string

Narrative summary of the overall feature strategy.

object[]

Web sources the pipeline consulted during the gather stage.

Returns 409 if the job has not yet completed. Poll GET /feature-research/{job_id}/status first and only call this endpoint when status is completed.

Stream feature research events

Subscribe to live progress for a feature research job. The stream uses cursor-based replay — reconnect at any sequence number and receive all missed events before switching to live delivery.

number

default:"0"

Resume from this event sequence number. Set to 0 to start from the beginning.

curl -N "https://api.manticscore.com/feature-research/d2e7b4c1-.../events?cursor=0" \
  -H "Authorization: Bearer <token>"

import httpx

with httpx.stream(
    "GET",
    "https://api.manticscore.com/feature-research/d2e7b4c1-.../events",
    headers={"Authorization": "Bearer <token>"},
    params={"cursor": 0},
) as resp:
    for line in resp.iter_lines():
        if line:
            print(line)

Each line is a JSON object with shape {"v": 1, "event": "<type>", "data": {...}}. The event types are:

Event	Description
`stage`	A pipeline stage (`scope`, `gather`, `analyze`, `synthesize`) started or completed.
`progress`	Free-text progress message within a stage.
`feature_analyzed`	A single feature analysis completed. Data contains the feature ID and analysis output.
`result`	All features analyzed. Contains the full results payload.
`error`	A fatal error occurred. Contains `message`, `code`, and `retryable`.
`done`	Stream is closed. Always the last event.

If you connect with cursor=0 to a completed job, the server fast-paths and returns the full result as a single result event. You don’t need to replay the full event log.

Auto-chaining behavior

Feature deep research can be triggered automatically in two ways: From market research: Call POST /research with "mode": "feature". When the market research pipeline completes, it automatically starts a feature deep research job on the top 5 features it identified. You don’t need to call POST /feature-research manually. From build graphs: Call POST /build-graphs without a feature_research_id. The server automatically detects the latest completed feature research job for the project and injects its data into the LLM prompt.

When a feature deep research job completes, the platform fires a push notification with type: "feature_research_complete". If you’re building a mobile client, listen for this notification to know when results are ready.

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Feature deep research API — multi-competitor analysis

Start feature deep research

Get job metadata

Get job status (lightweight)

Get full results

Stream feature research events

Auto-chaining behavior

​Start feature deep research

​Get job metadata

​Get job status (lightweight)

​Get full results

​Stream feature research events

​Auto-chaining behavior

Start feature deep research

Get job metadata

Get job status (lightweight)

Get full results

Stream feature research events

Auto-chaining behavior