Superlinked

AI that summarizes an accident report is easy. AI that never adds a claim the report doesn't make? That's the hard part, and it's the part that matters! The test case: the NTSB's East Palestine report. Three wayside detectors recorded the same railcar bearing running hotter and hotter (38°F, then 103°F, then 253°F above ambient) in the hours before the derailment. This pipeline reconstructs that sequence as a cited timeline: locations, times, temperatures, alerts, recipients. Every value traces to an exact span in the report, and the run halts if required evidence is missing. The repo even kept its failed experiment (a schema-based approach that didn't survive contact with the material) committed as diagnostic evidence. That honesty is half the value. For anyone building incident-review tooling: superlinked.com/blog/mainten…

Most healthcare document AI can't be validated. The claims data is locked behind privacy walls, and synthetic data proves nothing. CMS accidentally solved this: they published a documentation finding, complete with the answer. The rule required a face-to-face encounter within six months before delivery. The record showed seven. Result: insufficient documentation, payment recouped. The regulator defined what "correct" means, so anyone can check the work. Our example reproduces that finding end to end with five chained models, extracts the exact phrases ("L1851", "6 months", "7 months", "recoups payment"), and computes the one-month violation deterministically in code. All five required facts present, or the run halts rather than guesses. The version of healthcare AI a compliance officer can sign off on: superlinked.com/blog/prior-a…

SIE takes your JSON Schema and returns typed fields from a document using Qwen3.8 27B. The hard part isn’t generating braces. It's making `injured_people: 1` mean the source actually says someone was injured. Your parser won't catch a plausible lie.

In SIE's extraction example, the source mentions a bruised passenger. Sonnet 5 returns `injured_people: 0`; Qwen3.8 27B on SIE returns `1`. Both outputs look tidy. Only one belongs in the database.

Qwen3Guard 4B on SIE returns a Safety verdict and harm categories. Your app decides whether to hold the request. No dramatic refusal monologue required. Just a specialist doing one job before the main model speaks. superlinked.com/guardrails

50,000 tokens per second only matters when the comparison conditions are clear. We tested @topk_io `topk-embed-v1-small` through the Superlinked Inference Engine against its Hugging Face pipeline. Both pipelines used one NVIDIA L40S, bf16 precision, identical inputs and warmed-up Python calls. The results: • 48,000 to 52,000 document tokens/s • 1.5× higher document throughput • 7.1× higher query throughput • Single-query latency fell from 81 ms to 6 ms Every test query still returned the same top document. The useful lesson goes beyond this model. Model performance depends heavily on the runtime around it. Batching, GPU kernels, memory use and request scheduling can change the economics of an embedding workload without changing its checkpoint. This matters for teams searching across text and page images, especially when late-interaction models need to run at production volume. The article includes the benchmark setup, results by document length, quality checks and instructions for running both TopK models through SIE. Read it here: superlinked.com/blog/run-top… #Embeddings #VectorSearch #AIInfrastructure #OpenSourceAI

Love building with ML - and getting a room full of developers excited about it? We're hiring a DevRel Engineer at Superlinked to grow the community around our open source Inference Engine. You'll speak at conferences, run workshops and hackathons, build demos worth trying, and turn developer conversations into a better product. We're looking for someone who can ship code, tell a clear technical story, and bring people together, with a strong focus on the SF AI/ML scene. Sound like you? Come join us! jobs.superlinked.com/jobs/85… #hiring #devrel

When a company restates its earnings, can your document AI tell the old number from the new one? Get that wrong and you haven't automated analysis. You've automated a compliance incident. The test case is real: Pathward Financial reported $45.096M net income, then restated it to $36.080M, with language that earlier filings "should no longer be relied upon." Three documents, one figure, and attribution is the entire product. This example traces the number through all three SEC filings. Two extraction models must independently find every required span, or the run halts. Fail closed, by design: a pipeline that says "I couldn't verify this" is annoying; one that confidently reports a stale figure is dangerous. Follow one number all the way down: superlinked.com/blog/financi…

Before your agent answers, SIE can run the request through Qwen3Guard 4B. In one test, OpenAI Moderation returned `flagged: false` for a harmful request. SIE marked it Unsafe. The bouncer works better at the door than in the incident report.

You're laughing now. But come Monday, you'll be watching your coworker try to cram OCR, embeddings, a reranker and an LLM onto one GPU. All loaded at once. Use SIE. Let the teddy bear keep his legs. 😉 youtube.com/watch?v=qq58DM6F…

Can AI read an insurance policy well enough to separate covered costs from excluded ones? FEMA published a real appeal decision, so we could actually test it. The claim: $182,552 after a Lake Ontario flood. The barge quote alone: $181,832.94. What the policy covered: removing 12 to 15 cubic yards of stone from beneath the building. Not the barge, not the handling, not the disposal. Four chained models do the reading (parse, extract, rerank, generate) and the output lands where FEMA landed, checkable line by line against the published decision. It doesn't approve or deny anything. It turns a document pile into organized evidence so the adjuster starts from analysis instead of reading. That's the version of claims AI worth building right now. Compare it against FEMA's decision yourself: superlinked.com/blog/insuran…

In our 4,768 prompt safety eval, Qwen3Guard 4B on SIE reached 82.7% F1. GPT-6 Sol reached 76.1%. Turns out the model checking the door doesn't have to be the biggest one in the building.

Rekognition looked at a bed frame and confidently called it a coffee table. SIE's SigLIP ranked "bed" first from the store's own labels. 100% confidence in the wrong furniture is still the wrong furniture.

On our 500 document extraction eval, Qwen3.8 27B on SIE got 83.5% of field values exactly right, versus 79.3% for Sonnet 5. The schema can be valid while the answer is wrong. We measured the values. Check this out: superlinked.com/structured-o…

On 938 held-out product photos, SigLIP on SIE got type, color and material all right 72.3% of the time. Rekognition: 26.8%. Estimated list-rate cost per million photos: $24 vs $1,750. Finally, a labeling project finance won't hate. :) superlinked.com/image-classi…