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Superlinked gives your agent one API to read submitted records, retrieve current rules, extract evidence and return cited findings for human review.

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See how our example agent checked CMS's published L1851 case

Your documentation review agent

Read the submitted record, check current criteria and cite the mismatch.

Review question Does the L1851 record meet the face-to-face timing requirement?
Reviewer finding No. CMS’s example records 7 months; the window is 6.

Your review context

Submitted records, current rules and prior review decisions.

CMS Compliance guidance Lower limb orthoses
ORDER Written order L1851
RECORD Practitioner record Medical necessity
POD Proof of delivery Face-to-face · 7 months
Browse all tasks
from sie_sdk import SIEClient
​
client = SIEClient(api_key="sk-sie-…")
​
# Parse a document → clean markdown, tables and layout kept.
result = client.extract(
"modeldocling",
{"document": fileHTMLcms-lower-limb-orthoses.htmlbrowse},
)
print(result["data"]["markdown"])
document_bytes=$(base64 < 'cms-lower-limb-orthoses.html' | tr -d '\n')
curl https://api.superlinked.com/v1/extract/docling \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"document\":{\"data\":\"$document_bytes\",\"format\":\"html\"}}]}"
Build the "Doc to Markdown" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: docling (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: an uploaded document (PDF / Office / scan).
- Behaviour: return clean markdown for the document, preserving tables and reading order
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.
Input HTML pagecms-l1851-insufficient-documentation.html
CMS HTML page crop stating the six-month face-to-face encounter requirementCMS HTML page crop showing the published L1851 insufficient-documentation example
import numpy as np
from sie_sdk import SIEClient
​
client = SIEClient(api_key="sk-sie-…")
​
query = "queryFor CMS's published L1851 example, what was required, what was submitted, what timing gap caused insufficient documentation, and what payment action followed?"
documents = [
"document 1https://www.cms.gov/training-education/medicare-learning-networkr-mln/compliance/medicare-provider-compliance-tips/lower-limb-orthoses Page Last Modified: 02/11/2026 04:18 PM",
"document 2## Billing &amp; Coding Criteria",
"document 3We require prior authorization, a face-to-face encounter, and written order prior to delivery for HCPCS codes L1832 and L1851. Conduct the face-to-face encounter within the 6 months before prescribing the item.",
"document 4## Example of Improper Payments Due to Insufficient Documentation for Lower Limb Orthoses",
"document 5A supplier bills the claim for L1851 (Knee orthosis (KO), single upright, thigh and calf, with adjustable flexion and extension joint (unicentric or polycentric), medial-lateral and rotation control, with or without varus/valgus adjustment, prefabricated, off-the-shelf) and submits the following documentation per the review contractor's request:",
"document 6- Standard written order with correct HCPCS coding",
"document 7- Treating practitioner's medical record that has adequate medical necessity information",
"document 8- Proof of delivery with face-to-face encounter 7 months ago",
"document 9### What Documentation Was Missing?",
"document 10The doctor didn't document the face-to-face encounter within 6 months of proof of delivery.",
"document 11The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment.",
]
​
model = "modelQwen/Qwen3-Embedding-4B"
q = np.array(client.encode(model, [{"text": query}], is_query=True)[0]["dense"])
docs = client.encode(model, [{"text": d} for d in documents])
mat = np.array([v["dense"] for v in docs])
scores = mat @ q / (np.linalg.norm(mat, axis=1) * np.linalg.norm(q)) # cosine
for i in np.argsort(scores)[::-1]:
print(f"{scores[i]:.3f} {documents[i]}")
import { unpackMessage } from '@superlinked/sie-sdk';

const query = "For CMS's published L1851 example, what was required, what was submitted, what timing gap caused insufficient documentation, and what payment action followed?";
const documents = [
  "https://www.cms.gov/training-education/medicare-learning-networkr-mln/compliance/medicare-provider-compliance-tips/lower-limb-orthoses Page Last Modified: 02/11/2026 04:18 PM",
  "## Billing &amp; Coding Criteria",
  "We require prior authorization, a face-to-face encounter, and written order prior to delivery for HCPCS codes L1832 and L1851. Conduct the face-to-face encounter within the 6 months before prescribing the item.",
  "## Example of Improper Payments Due to Insufficient Documentation for Lower Limb Orthoses",
  "A supplier bills the claim for L1851 (Knee orthosis (KO), single upright, thigh and calf, with adjustable flexion and extension joint (unicentric or polycentric), medial-lateral and rotation control, with or without varus/valgus adjustment, prefabricated, off-the-shelf) and submits the following documentation per the review contractor's request:",
  "- Standard written order with correct HCPCS coding",
  "- Treating practitioner's medical record that has adequate medical necessity information",
  "- Proof of delivery with face-to-face encounter 7 months ago",
  "### What Documentation Was Missing?",
  "The doctor didn't document the face-to-face encounter within 6 months of proof of delivery.",
  "The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment.",
];

async function encode(texts: string[], isQuery = false): Promise<Float32Array[]> {
  const response = await fetch("https://api.superlinked.com/v1/encode/Qwen%2FQwen3-Embedding-4B", {
    method: 'POST',
    signal: AbortSignal.timeout(30_000),
    headers: {
      Authorization: "Bearer sk-sie-…",
      'Content-Type': 'application/json',
      Accept: 'application/msgpack',
    },
    body: JSON.stringify({
      items: texts.map((text) => ({ text })),
      params: { output_types: ['dense'], ...(isQuery ? { options: { is_query: true } } : {}) },
    }),
  });
  if (!response.ok) throw new Error(`SIE returned ${response.status}`);
  const result = unpackMessage<{ items: { dense: { values: Float32Array } }[] }>(
    new Uint8Array(await response.arrayBuffer()),
  );
  return result.items.map((item) => item.dense.values);
}

const [q] = await encode([query], true);
const docs = await encode(documents);
const cosine = (a: Float32Array, b: Float32Array) => {
  const dot = a.reduce((s, x, i) => s + x * b[i]!, 0);
  return dot / (Math.hypot(...a) * Math.hypot(...b));
};
const ranked = documents
  .map((doc, i) => ({ doc, score: cosine(q!, docs[i]!) }))
  .sort((a, b) => b.score - a.score);
console.log(ranked);
# Encode the query with is_query, then the candidates; rank by cosine.
curl https://api.superlinked.com/v1/encode/Qwen%2FQwen3-Embedding-4B \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"text\":\"For CMS's published L1851 example, what was required, what was submitted, what timing gap caused insufficient documentation, and what payment action followed?\"}],\"params\":{\"output_types\":[\"dense\"],\"options\":{\"is_query\":true}}}"

curl https://api.superlinked.com/v1/encode/Qwen%2FQwen3-Embedding-4B \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"text\":\"https://www.cms.gov/training-education/medicare-learning-networkr-mln/compliance/medicare-provider-compliance-tips/lower-limb-orthoses Page Last Modified: 02/11/2026 04:18 PM\"},{\"text\":\"## Billing &amp; Coding Criteria\"},{\"text\":\"We require prior authorization, a face-to-face encounter, and written order prior to delivery for HCPCS codes L1832 and L1851. Conduct the face-to-face encounter within the 6 months before prescribing the item.\"},{\"text\":\"## Example of Improper Payments Due to Insufficient Documentation for Lower Limb Orthoses\"},{\"text\":\"A supplier bills the claim for L1851 (Knee orthosis (KO), single upright, thigh and calf, with adjustable flexion and extension joint (unicentric or polycentric), medial-lateral and rotation control, with or without varus/valgus adjustment, prefabricated, off-the-shelf) and submits the following documentation per the review contractor's request:\"},{\"text\":\"- Standard written order with correct HCPCS coding\"},{\"text\":\"- Treating practitioner's medical record that has adequate medical necessity information\"},{\"text\":\"- Proof of delivery with face-to-face encounter 7 months ago\"},{\"text\":\"### What Documentation Was Missing?\"},{\"text\":\"The doctor didn't document the face-to-face encounter within 6 months of proof of delivery.\"},{\"text\":\"The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment.\"}],\"params\":{\"output_types\":[\"dense\"]}}"
Build the "Search" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: Qwen/Qwen3-Embedding-4B (SIE primitive: /encode). Keep the model id configurable.

Task
- Input: a query string plus a list of candidate documents.
- Behaviour: return the candidates ranked by semantic similarity to the query, with a score per candidate
- Send two POST /v1/encode requests: one item {text: query} with params {output_types: ["dense"], options: {is_query: true}}, then all candidate documents with params {output_types: ["dense"]}. Rank every candidate by cosine similarity between its decoded dense vector and the query vector. In Python, use encode(..., is_query=True) only for the query call. In TypeScript, send those native HTTP bodies and decode MessagePack with the public unpackMessage export. Do not issue one request per candidate.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.
from sie_sdk import SIEClient
​
client = SIEClient(api_key="sk-sie-…")
​
query = "queryFor CMS's published L1851 example, what was required, what was submitted, what timing gap caused insufficient documentation, and what payment action followed?"
documents = [
"document 1https://www.cms.gov/training-education/medicare-learning-networkr-mln/compliance/medicare-provider-compliance-tips/lower-limb-orthoses Page Last Modified: 02/11/2026 04:18 PM",
"document 2## Billing &amp; Coding Criteria",
"document 3We require prior authorization, a face-to-face encounter, and written order prior to delivery for HCPCS codes L1832 and L1851. Conduct the face-to-face encounter within the 6 months before prescribing the item.",
"document 4## Example of Improper Payments Due to Insufficient Documentation for Lower Limb Orthoses",
"document 5A supplier bills the claim for L1851 (Knee orthosis (KO), single upright, thigh and calf, with adjustable flexion and extension joint (unicentric or polycentric), medial-lateral and rotation control, with or without varus/valgus adjustment, prefabricated, off-the-shelf) and submits the following documentation per the review contractor's request:",
"document 6- Standard written order with correct HCPCS coding",
"document 7- Treating practitioner's medical record that has adequate medical necessity information",
"document 8- Proof of delivery with face-to-face encounter 7 months ago",
"document 9### What Documentation Was Missing?",
"document 10The doctor didn't document the face-to-face encounter within 6 months of proof of delivery.",
"document 11The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment.",
]
items = [{"id": str(i), "text": d} for i, d in enumerate(documents)]
ranked = client.score("modelQwen/Qwen3-Reranker-4B", {"text": query}, items)
for r in ranked["scores"]:
print(r["score"], documents[int(r["item_id"])])
import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const query = "For CMS's published L1851 example, what was required, what was submitted, what timing gap caused insufficient documentation, and what payment action followed?";
const documents = [
  "https://www.cms.gov/training-education/medicare-learning-networkr-mln/compliance/medicare-provider-compliance-tips/lower-limb-orthoses Page Last Modified: 02/11/2026 04:18 PM",
  "## Billing &amp; Coding Criteria",
  "We require prior authorization, a face-to-face encounter, and written order prior to delivery for HCPCS codes L1832 and L1851. Conduct the face-to-face encounter within the 6 months before prescribing the item.",
  "## Example of Improper Payments Due to Insufficient Documentation for Lower Limb Orthoses",
  "A supplier bills the claim for L1851 (Knee orthosis (KO), single upright, thigh and calf, with adjustable flexion and extension joint (unicentric or polycentric), medial-lateral and rotation control, with or without varus/valgus adjustment, prefabricated, off-the-shelf) and submits the following documentation per the review contractor's request:",
  "- Standard written order with correct HCPCS coding",
  "- Treating practitioner's medical record that has adequate medical necessity information",
  "- Proof of delivery with face-to-face encounter 7 months ago",
  "### What Documentation Was Missing?",
  "The doctor didn't document the face-to-face encounter within 6 months of proof of delivery.",
  "The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment.",
];
const items = documents.map((text, i) => ({ id: String(i), text }));
const ranked = await client.score('Qwen/Qwen3-Reranker-4B', { text: query }, items);
console.log(ranked.scores.map(({ itemId, score }) => ({ document: documents[Number(itemId)], score })));
curl https://api.superlinked.com/v1/score/Qwen%2FQwen3-Reranker-4B \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"query\":{\"text\":\"For CMS's published L1851 example, what was required, what was submitted, what timing gap caused insufficient documentation, and what payment action followed?\"},\"items\":[{\"id\":\"0\",\"text\":\"https://www.cms.gov/training-education/medicare-learning-networkr-mln/compliance/medicare-provider-compliance-tips/lower-limb-orthoses Page Last Modified: 02/11/2026 04:18 PM\"},{\"id\":\"1\",\"text\":\"## Billing &amp; Coding Criteria\"},{\"id\":\"2\",\"text\":\"We require prior authorization, a face-to-face encounter, and written order prior to delivery for HCPCS codes L1832 and L1851. Conduct the face-to-face encounter within the 6 months before prescribing the item.\"},{\"id\":\"3\",\"text\":\"## Example of Improper Payments Due to Insufficient Documentation for Lower Limb Orthoses\"},{\"id\":\"4\",\"text\":\"A supplier bills the claim for L1851 (Knee orthosis (KO), single upright, thigh and calf, with adjustable flexion and extension joint (unicentric or polycentric), medial-lateral and rotation control, with or without varus/valgus adjustment, prefabricated, off-the-shelf) and submits the following documentation per the review contractor's request:\"},{\"id\":\"5\",\"text\":\"- Standard written order with correct HCPCS coding\"},{\"id\":\"6\",\"text\":\"- Treating practitioner's medical record that has adequate medical necessity information\"},{\"id\":\"7\",\"text\":\"- Proof of delivery with face-to-face encounter 7 months ago\"},{\"id\":\"8\",\"text\":\"### What Documentation Was Missing?\"},{\"id\":\"9\",\"text\":\"The doctor didn't document the face-to-face encounter within 6 months of proof of delivery.\"},{\"id\":\"10\",\"text\":\"The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment.\"}]}"
Build the "Rerank" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: Qwen/Qwen3-Reranker-4B (SIE primitive: /score). Keep the model id configurable.

Task
- Input: a query string plus a list of candidate documents.
- Behaviour: reorder the candidates by true relevance to the query using the cross-encoder
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.
from sie_sdk import SIEClient
​
client = SIEClient(api_key="sk-sie-…")
​
result = client.extract(
"modelfastino/gliner2-large-v1",
{"text": "textThe doctor didn't document the face-to-face encounter within 6 months of proof of delivery. The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment."},
labels=[
"entity typemissing documentation",
"entity typeclaim result",
"entity typepayment action",
"entity typeorganization",
"entity typetime period",
],
)
for span in result["entities"]:
print(span["label"], span["text"])
import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const result = await client.extract(
  'fastino/gliner2-large-v1',
  { text: "The doctor didn't document the face-to-face encounter within 6 months of proof of delivery. The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment." },
  { labels: ["missing documentation","claim result","payment action","organization","time period"] },
);
console.log(result.entities);
curl https://api.superlinked.com/v1/extract/fastino%2Fgliner2-large-v1 \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"text\":\"The doctor didn't document the face-to-face encounter within 6 months of proof of delivery. The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment.\"}],\"params\":{\"labels\":[\"missing documentation\",\"claim result\",\"payment action\",\"organization\",\"time period\"]}}"
Build the "Named entities" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: fastino/gliner2-large-v1 (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: a block of text.
- Behaviour: return the named entities found in the text with their types
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.
Output

Lower Limb Orthoses

Billing & Coding Criteria

Conduct the face-to-face encounter within the 6 months before prescribing the item.

Published L1851 example

  • Standard written order with correct HCPCS coding
  • Treating practitioner's medical record
  • Proof of delivery with face-to-face encounter 7 months ago

Missing documentation

The doctor didn't document the face-to-face encounter within 6 months of proof of delivery.

  • Extracts clean, agent-ready markdown
  • Keeps tables and layout intact
Output
## Example of Improper Payments Due to Insufficient Documentation for Lower Limb Orthoses
0.62
### What Documentation Was Missing?
0.57
We require prior authorization, a face-to-face encounter, and written order prior to delivery for HCPCS codes L1832 and L1851. Conduct the face-to-face encounter within the 6 months before prescribing the item.
0.55
The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment.
0.53
A supplier bills the claim for L1851 (Knee orthosis (KO), single upright, thigh and calf, with adjustable flexion and extension joint (unicentric or polycentric), medial-lateral and rotation control, with or without varus/valgus adjustment, prefabricated, off-the-shelf) and submits the following documentation per the review contractor's request:
0.51
https://www.cms.gov/training-education/medicare-learning-networkr-mln/compliance/medicare-provider-compliance-tips/lower-limb-orthoses Page Last Modified: 02/11/2026 04:18 PM
0.45
The doctor didn't document the face-to-face encounter within 6 months of proof of delivery.
0.44
- Proof of delivery with face-to-face encounter 7 months ago
0.44
## Billing &amp; Coding Criteria
0.44
- Standard written order with correct HCPCS coding
0.41
- Treating practitioner's medical record that has adequate medical necessity information
0.41
Top match at 0.62 · 11 candidates ranked
  • Ranks by meaning, not keyword overlap
  • Scores every candidate against your query
Output
The review contractor completes the claim as an insufficient documentation error, and the MAC recoups payment.
↑ up 100.98
## Example of Improper Payments Due to Insufficient Documentation for Lower Limb Orthoses
↑ up 20.87
The doctor didn't document the face-to-face encounter within 6 months of proof of delivery.
↑ up 70.68
A supplier bills the claim for L1851 (Knee orthosis (KO), single upright, thigh and calf, with adjustable flexion and extension joint (unicentric or polycentric), medial-lateral and rotation control, with or without varus/valgus adjustment, prefabricated, off-the-shelf) and submits the following documentation per the review contractor's request:
↑ up 10.65
We require prior authorization, a face-to-face encounter, and written order prior to delivery for HCPCS codes L1832 and L1851. Conduct the face-to-face encounter within the 6 months before prescribing the item.
↓ down 20.59
### What Documentation Was Missing?
↑ up 30.17
- Standard written order with correct HCPCS coding
↓ down 10.10
- Proof of delivery with face-to-face encounter 7 months ago
– held0.04
https://www.cms.gov/training-education/medicare-learning-networkr-mln/compliance/medicare-provider-compliance-tips/lower-limb-orthoses Page Last Modified: 02/11/2026 04:18 PM
↓ down 80.00
## Billing &amp; Coding Criteria
↓ down 80.00
- Treating practitioner's medical record that has adequate medical necessity information
↓ down 40.00
  • Reorders by true relevance
  • Scored by meaning, keyword-free
Output

The doctororganization didn't document the face-to-face encountermissing documentation within 6 monthstime period of proof of delivery. The review contractororganization completes the claim as an insufficient documentation errorclaim result, and the MACorganization recoups paymentpayment action.

doctororganizationface-to-face encountermissing documentation6 monthstime periodreview contractororganizationinsufficient documentation errorclaim resultMACorganizationrecoups paymentpayment action
7 matches across 5 types
  • Pulls typed entities straight from raw text

Compare models for this task

PRICE
$ / 1M tokens ↓
$0.02 Voyage 4 Lite
$0.02 OpenAI text-embedding-3-small
$0.06 SIE Qwen3 Embedding 4B SIE Qwen3 Embedding 4B $0.06
Voyage 4 Large $0.12
OpenAI text-embedding-3-large $0.13
QUALITY
Right result first · 6,200 questions ↑
Voyage 4 Large 0.547
SIE Qwen3 Embedding 4B 0.546
OpenAI text-embedding-3-large 0.508
Voyage 4 Lite 0.505
OpenAI text-embedding-3-small 0.475
LATENCY
median ↓
16ms SIE Qwen3 Embedding 4B
OpenAI text-embedding-3-large 238ms
PRICE
$ / 100,000 queries ↓
$15 SIE Qwen3 Reranker 4B
$17 Voyage rerank-3
$52 Claude Haiku 5.5 (thinking off) Claude Haiku 5.5 (thinking off) $52
$86 GPT-6 Luna (high reasoning) GPT-6 Luna (high reasoning) $86
Cohere Rerank 4 Fast $200
Cohere Rerank 4 Pro $250
QUALITY
Right document first · 499 questions ↑
OpenAI GPT-6 Luna (high reasoning) 0.984 GPT-6 Luna (high reasoning) 0.984
Voyage rerank-3 0.926
SIE Qwen3 Reranker 4B 0.912
Cohere Rerank 4 Pro 0.842
Anthropic Claude Haiku 5.5 (thinking off) 0.838 Claude Haiku 5.5 (thinking off) 0.838 Claude Haiku 5.5 (thinking off) 0.838
Cohere Rerank 4 Fast 0.715 Cohere Rerank 4 Fast 0.715
LATENCY
median ↓
55ms SIE Qwen3 Reranker 4B
91ms Voyage rerank-3
105ms Cohere Rerank 4 Fast
179ms Cohere Rerank 4 Pro
Anthropic Claude Haiku 5.5 (thinking off) 1.3s Claude Haiku 5.5 (thinking off) 1.3s
PRICE
$ / 1M input tokens ↓
$0.05 SIE NuNER Zero-span
$0.1 OpenAI GPT-6 Luna
$0.1 Anthropic Claude Haiku 5.5
OpenAI GPT-6.1 Sol $2
Anthropic Claude Sonnet 5.5 $2
QUALITY
F1 · custom entity types ↑
Anthropic Claude Sonnet 5.5 0.796 Claude Sonnet 5.5 0.796
OpenAI GPT-6.1 Sol 0.792 GPT-6.1 Sol 0.792
Anthropic Claude Haiku 5.5 0.737 Claude Haiku 5.5 0.737
OpenAI GPT-6 Luna 0.694 GPT-6 Luna 0.694
SIE NuNER Zero-span 0.630
AWS Comprehend 0.390 0.390 AWS Comprehend
PRICE
$ / 1k pages ↓
$1.02 OpenAI GPT-6 Luna $1.02 GPT-6 Luna
$1.24 Anthropic Claude Haiku 5.5 $1.24 Claude Haiku 5.5
$2.60 SIE LightOnOCR-3
$4.00 Mistral OCR 4.1
Anthropic Claude Sonnet 5.5 $25.42 Claude Sonnet 5.5 $25.42
QUALITY
Checks passed · olmOCR-Bench ↑
SIE LightOnOCR-3 0.851
Anthropic Claude Sonnet 5.5 0.841 Claude Sonnet 5.5 0.841
Mistral OCR 4.1 0.833
OpenAI GPT-6 Luna 0.806 GPT-6 Luna 0.806
Anthropic Claude Haiku 5.5 0.800 Claude Haiku 5.5 0.800

Our example agent catches the timing error in CMS’s published case

View on GitHub
CMS requirement Face-to-face encounter Within 6 months
Published example Proof of delivery Encounter 7 months ago

Five model stages assemble and check the record

Read the packet Docling Doc to Markdown
Retrieve active policy BGE-M3 Search
Prioritize the criterion Qwen3-Reranker-4B Rerank
Extract codes and findings GLiNER multi v2.1 Named entities
Validate exact source spans GLiNER2 large v1 Named entities
Evidence check

7 months 6-month limit

Insufficient documentation
7 mo submitted encounter age
6 mo required window
35.2% 2024 improper payment rate
$91.2M projected improper payments

Deploy your way

Self-host with K8s

Easy & scalable deployment in your own cloud.

  • Terraform to your cloud in minutes
  • Apache-2.0, same engine as Serverless
  • Scales to zero, no bill between jobs
  • Per-tenant pools, no noisy neighbors
Agent prompt
Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.Deploy SIE to our GCP project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.
Deploy guide

Run locally

Run the same models on your own machine.

  • Runs on NVIDIA GPU or Apple Silicon
  • One command, no Docker or cluster
  • All 100+ Serverless models, fully offline
  • Same SDK and IDs, no code changes
pip install "sie-server[local]" && sie-server servepip install "sie-server[local]" && sie-server serve --device cuda
Quickstart

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