Plug Prism into your product. Prism for developers.

One API live, four in preview

Each is the production-tested core of an existing Prism marketplace module, exposed so you can ship the same capability inside your product.

Hallucination Guard API

Available now

Citation cross-walking for legal, financial, and insurance AI output. Inspect generated text for case citations, statute references, policy provisions, and reporter cites; any reference that doesn't trace back to user-provided source material gets flagged inline with [verify] markers and a structured risk score. Prism's modules are deliberately purpose-built — each handles its own scoped data. The Hallucination Guard API works the same way: stateless verification, no cross-tenant data, no learning from your inputs. Your sources stay yours.

Get API key

Built for

·Legal-tech startups shipping AI-assisted drafting tools
·Insurance-tech building claims advocacy / policy review
·Financial advisor software writing under FINRA / SEC rules
·Healthcare-AI products surfacing medical references
·Any company shipping AI-generated text where a fabricated citation creates real liability

Use cases

·Verify case citations in lawyer drafting tools before they reach a partner's review
·Flag invented statute references in compliance documents
·Check policy citations in insurance advocacy letters against the actual policy PDF
·Validate claims in financial advisor communications under FINRA rules
·Catch hallucinated medical references in healthcare AI
·Guard against fabricated source attributions in research-assistant tools

Sample request & response

JSONread-only preview

POST https://prism.zi-find.com/api/v1/guard/verify-references

{
  "text": "Per Smith v. Jones, 142 F.3d 891 (9th Cir. 2018), the doctrine applies...",
  "sources": [
    "Memorandum of intake notes...",
    "Exhibit B - policy excerpt..."
  ],
  "domain": "legal"
}

→ 200 OK
{
  "flagged_text": "Per Smith v. Jones, 142 F.3d 891 (9th Cir. 2018) [verify], the doctrine applies...",
  "flags": [
    {
      "type": "case_citation",
      "raw": "Smith v. Jones, 142 F.3d 891 (9th Cir. 2018)",
      "offset_start": 4,
      "offset_end": 47,
      "in_sources": false
    }
  ],
  "risk_score": 0.72,
  "domain": "legal"
}

How it works behind the scenes

Domain-tuned regex sweeps cover case names with 'v.', U.S.C./C.F.R. citations, reporter cites, and Section/§ references, then cross-walk every match against user-supplied source material. Cross-walking is whitespace- and case-insensitive so paraphrased citations still resolve. Different domains (legal vs insurance vs financial) get different patterns. Tuned in production across nine professional-vertical Prism modules.

Why developers choose Prism over rolling their own

✓Pre-tuned across real production traffic. Patterns aren't theoretical.
✓Idempotent and offset-safe. Re-running on already-flagged text is a no-op, never corrupts mid-text.
✓Domain-specific configurations out of the box (legal, insurance, financial).

Or try the hosted module: Loss Run Analyzer

Voice Profile Engine API

Coming soon

Profile crystallization that turns a writer's samples into a portable voice profile, then generates new content in that voice forever. The voice/inspiration split, separating WHO writes from WHAT structure, keeps the generated output distinctly the user's voice even when borrowing structural patterns from other creators.

Built for

·Ghostwriter platforms
·Newsletter tools (Substack / Beehiiv extensions)
·Social schedulers competing on quality of output, not just queue management
·Executive coaching software
·Content agencies serving multiple clients

Use cases

·Add 'write in your voice' to any AI writing tool
·Power newsletter platforms with author-voice generation
·Let coaches give clients an AI that writes like them
·Build personal-brand SaaS for executives
·Power agency tools that write in client voice
·Layer voice generation onto AI sales-email products

Sample request & response

JSONread-only preview

POST https://api.prism.dev/v1/voice-profile/train

{ "samples": ["...post 1...", "...post 2...", "..."] }

→ 200 OK
{
  "profile_id": "vp_8c3...",
  "summary": "Direct, opinionated, lean. Short sentences. Stacks single-line paragraphs.",
  "sample_count": 22
}

POST https://api.prism.dev/v1/voice-profile/generate

{
  "profile_id": "vp_8c3...",
  "topic": "What surprised me about Q2 retention",
  "format": "linkedin"
}

→ 200 OK
{
  "variants": [
    { "content": "...", "char_count": 1124 },
    { "content": "...", "char_count": 987 },
    { "content": "...", "char_count": 1308 }
  ]
}

How it works behind the scenes

Profile crystallization extracts vocabulary signatures, sentence rhythm, opinion patterns, things the writer avoids, tonal range, and audience cues. The result is a portable JSON profile (~3-5KB) that can be exported, re-imported, and reused across applications. Generation runs the profile against the prompt with strict rules that prevent voice contamination from optional structural references.

Why developers choose Prism over rolling their own

✓The voice/inspiration split is the differentiator. Competitors mix these and produce averaged voice.
✓Profiles are portable: train once on Prism, export the JSON, reuse anywhere
✓Tested against deliberately tough cases (multiple voices in one corpus, sparse samples, stylistic outliers)

Or try the hosted module: Content Studio

Custom Calculator Engine API

Coming soon

Embeddable profit-calculator builder with PRISM dimensional analysis on top. Define schemas with custom revenue lines, expense lines, and cross-line formulas. The math runs deterministically server-side; an analysis layer reads inputs and outputs and surfaces what's killing margin, what's working, and what to change ranked by impact.

Built for

·Bookkeeping / accounting software that wants AI-powered P&L commentary
·Industry-specific SaaS (STR ops, agencies, ecommerce platforms)
·Coaching platforms shipping calculators to students
·Financial planning tools

Use cases

·Add AI-powered P&L analysis to bookkeeping tools
·Let coaches define calculator templates for students to fill in
·Power industry SaaS with custom profit modeling
·Embed deal-vetting math in real estate tools
·Build white-label financial calculators for agencies

Sample request & response

JSONread-only preview

POST https://api.prism.dev/v1/calculator-engine/schemas

{
  "name": "Airbnb Arbitrage P&L",
  "lines": [
    { "id": "rev_nights", "label": "Nightly revenue", "formula": "adr * occupancy * 30" },
    { "id": "exp_rent", "label": "Rent", "type": "expense" },
    { "id": "exp_furnishing_amort", "label": "Furnishing amort.", "type": "expense" },
    { "id": "net", "label": "Net monthly", "formula": "rev_nights - exp_rent - exp_furnishing_amort" }
  ]
}

→ 200 OK
{ "schema_id": "calc_9e2..." }

POST https://api.prism.dev/v1/calculator-engine/run

{
  "schema_id": "calc_9e2...",
  "values": { "adr": 185, "occupancy": 0.72, "exp_rent": 2400, "exp_furnishing_amort": 350 }
}

→ 200 OK
{
  "outputs": { "rev_nights": 3996, "net": 1246 },
  "analysis": {
    "ranked_levers": [
      { "lever": "occupancy", "impact_per_pct": 55, "current": 0.72 },
      { "lever": "adr", "impact_per_dollar": 21.6 }
    ],
    "headline": "Net is positive but thin. Occupancy moves your margin twice as fast as ADR at this rent."
  }
}

How it works behind the scenes

Schema builder supports custom revenue and expense lines with formulas that can reference other lines. Math runs in a deterministic evaluation layer (no LLM in the calculation path). The PRISM dimensional analysis layer reads inputs + outputs and surfaces ranked levers, broken assumptions, and headline takeaways. The combination of static math and dynamic commentary is what most calculators are missing.

Why developers choose Prism over rolling their own

✓Most calculators are static. Yours is user-definable AND has commentary.
✓Math is deterministic and auditable. No surprises from a model picking different numbers.
✓Commentary layer ranks levers by impact, not just lists pros and cons.

Or try the hosted module: Custom Profit Calculator

Meeting Memory Search API

Coming soon

Corpus interrogation across an entire transcript history. Each transcript is structured-extracted for commitments, decisions, open questions, and action items. The query layer runs semantic search across the corpus; the digest layer surfaces what's slipping through the cracks on a weekly cadence.

Built for

·CRMs (Hubspot / Pipedrive integrations)
·Sales-enablement (Gong / Chorus alternatives at lower price points)
·Meeting notetakers (Otter / Fireflies competitors)
·Coaching platforms with recorded sessions
·Executive assistant software

Use cases

·Power 'search across all my calls' in any CRM
·Add commitment tracking to notetaker apps
·Let sales managers query what reps are committing to in client meetings
·Build coaching software that surfaces patterns across sessions
·Generate weekly executive briefings from meeting corpus

Sample request & response

JSONread-only preview

POST https://api.prism.dev/v1/meeting-memory/ingest

{
  "transcript_id": "tx_2024_q2_acme_call_03",
  "occurred_at": "2026-04-22T14:00:00Z",
  "participants": ["sarah@acme.com", "you@yours.com"],
  "transcript": "Sarah: ... You: ..."
}

→ 200 OK
{ "extracted": { "commitments": 3, "decisions": 1, "open_questions": 2 } }

POST https://api.prism.dev/v1/meeting-memory/query

{ "q": "every commitment Sarah made about Q2 budget" }

→ 200 OK
{
  "matches": [
    {
      "transcript_id": "tx_2024_q2_acme_call_03",
      "snippet": "Sarah: We can move the budget approval up to early June.",
      "kind": "commitment",
      "confidence": 0.91
    }
  ]
}

POST https://api.prism.dev/v1/meeting-memory/digest

{ "window": "last_7_days" }

→ 200 OK
{
  "slipping": [
    { "title": "You promised pricing follow-up to BluePeak (no email sent yet)", "owed_to": "kim@bluepeak.com" }
  ],
  "delivered": [...]
}

How it works behind the scenes

Each ingested transcript is structured-extracted at write time for commitments, decisions, open questions, and action items. Query runs semantic search over the corpus with the structured fields as a first-class filter. Weekly digest re-reads the structured store and produces what's-slipping reports without re-tokenizing the raw transcripts.

Why developers choose Prism over rolling their own

✓Commitment-extraction at ingest is the killer feature. Most tools index. Prism understands.
✓Weekly digest combines query + extraction without exposing two separate APIs
✓The structured layer makes 'what did Sarah promise' a one-call query, not a manual review

Or try the hosted module: Meeting Memory

Airbnb Deal Analyzer API

Coming soon

Property address in, deal verdict out. Behind one call: AirROI market data, arbitrage math, and a green/yellow/red verdict with reasoning. Buyers don't have to negotiate AirROI access. Prism wraps the data layer so you ship one integration instead of three.

Built for

·STR software (PriceLabs, Hospitable, Hostfully)
·Real estate platforms (Mashvisor competitors, Zillow Premier Agent equivalents)
·Rental arbitrage coaching software
·Real estate investor CRMs
·Real estate education companies

Use cases

·Add deal-vetting to STR ops platforms
·Power Airbnb investor tools with verdict API
·Let coaches hand students a deal-screening tool
·Embed in real estate CRMs for investor segments
·Build vertical-specific MLS alternatives

Sample request & response

JSONread-only preview

POST https://api.prism.dev/v1/airbnb-deal/analyze

{
  "address": "1234 Main St, Nashville, TN 37210",
  "rent_estimate_monthly": 2400,
  "furnishing_amortization_monthly": 350
}

→ 200 OK
{
  "address_normalized": "1234 Main St, Nashville, TN 37210",
  "market": {
    "projected_annual_revenue": 47800,
    "occupancy_rate": 0.71,
    "adr": 185,
    "comp_count": 41,
    "data_provider": "airroi"
  },
  "math": {
    "monthly_revenue": 3983,
    "monthly_costs": 2750,
    "monthly_net": 1233,
    "annual_net": 14796
  },
  "verdict": "yellow",
  "reasoning": "Net is positive but thin. Occupancy is on par with the comp set; ADR is below the 70th percentile. Worth a closer look but not an obvious buy.",
  "red_flags": [],
  "yellow_flags": ["ADR below market median for the comp set"]
}

How it works behind the scenes

AirROI access is gated and individually expensive to negotiate. Prism wraps it so you get the data + math + verdict in a single call. The math layer is deterministic; the verdict and reasoning layer runs the inputs through a structured analysis. Verdict bands (green / yellow / red) are tunable per integration if you want to encode your own arbitrage thresholds.

Why developers choose Prism over rolling their own

✓AirROI access is hard to get individually; one Prism integration replaces three
✓Math and verdict in one call. No client-side coordination.
✓Verdict thresholds tunable so you can encode your own deal philosophy

Or try the hosted module: Airbnb Deal Analyzer

Plug Prism into your product.

One API live, four in preview

Hallucination Guard API

Voice Profile Engine API

Custom Calculator Engine API

Meeting Memory Search API

Airbnb Deal Analyzer API

Pricing preview

Hobbyist

Startup

Scale

Enterprise

Not building a product? Prism is also a SaaS.

Join the waitlist