Inference for faster, cheaper & smarter legal agents
Balance capacity and privacy with one API for open and fine-tuned models, serverless and in your own cloud.
- GLiNER2.5 Decide
- Qwen3 Embedding 8B
- LightOnOCR-3
- Your fine-tuned model
- GLM-5.3 Flash
- DeepSeek V4.1 Flash
- Gemma 4 31B
The leading legal AI platforms already run on open models
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harvey.aiAug 20, 2026
Harvey Tenet Research Preview
…we partnered with Engram to train a Qwen3.8-27B model… completes tasks competitively with leading frontier models while reducing cost per query by 90%.
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thomsonreuters.comAug 24, 2026
Thomson Reuters Leverages its World-Class Data Assets to Launch Its Own Frontier Model
Thomson, the company’s proprietary LLM, was trained and is run at a fraction of the cost of comparable frontier models… Thomson starts from a strong open-source foundation.
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venturebeat.comMar 20, 2025
Small models as paralegals: LexisNexis distills models to build AI assistant
Right now, LexisNexis mostly relies on a fine-tuned Mistral model…
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Financial TimesSep 10, 2026
Latham & Watkins buys Nvidia servers to set up in-house AI systems
Law firm is customising open-weight models as alternative to OpenAI and Anthropic
Review at your own pace, not your provider’s rate limit
Each job runs on a model sized for it, next to your app. In your own cluster, throughput is the GPUs you add, not a quota a provider sets.
Run a month of legal document work for 78% less
Task-sized open models do each job for a fraction of a frontier API’s price. Each line is priced from its task page, at an illustrative month’s volume.
- Parse discovery productions
$8,000$4,100 Document checks passed: SIE 73.2% · Mistral 83.3% - Rerank research results
$12,500$744 Open model more accurate - Extract terms from agreements
$3,840$364 More values exactly right - Redact client data
$703$145 Less personal data exposed
Tuned on your task, an open model beats the frontier APIs at it
How each result was measured
- Contract-clause review
- LexGLUE UNFAIR-ToS: 3,882 Terms of Service clauses labelled by lawyers, none of them among the 5,532 the model was tuned on. F1 on the unfair class; each API at its best setting, with the lawyers’ annotation rulebook in its prompt.
- Possible fraud in bank messages
- BANKING77: 3,076 customer messages to a bank, 240 of them reporting a payment they don’t recognise or a card or phone lost, stolen or compromised, none among the 9,993 the model was tuned on. The share of those reports caught while 95% of the messages flagged are reports, each model’s threshold set on the scored messages; GPT-6 Luna at low effort.
- Business questions to chart specs
- Superlinked’s gd-1, August 2026: Gemma 4 31B tuned on generated questions, then scored once on a BI assistant’s 875-question benchmark, counting the answers that return the reference data. GPT-5.6 Sol at maximum reasoning was the strongest API. The training questions came from the benchmark’s own generator, which is the home advantage tuning on your own task gives.
Meet the confidentiality requirements of your law firm customers
| What the rule asks | SIE Serverless | SIE in your cloud |
|---|---|---|
Keep client data in a secure environment UK SRA warning notice, Aug 2026Firms “should satisfy themselves that client data remains within a secure environment” and that it “is not used to train AI models except where explicitly authorised”. | SOC 2 Type 2 audited In the US or EU region you choose | Inside your network Your cloud account or a client’s |
Get consent before disclosing it to a third party Florida Bar Ethics Opinion 24-1, Jan 2024“If the use of a generative AI program does not involve the disclosure of confidential information to a third-party, a lawyer is not required to obtain a client’s informed consent pursuant to Rule 4-1.6.” | Superlinked is a third party We process requests as your vendor | No third party Superlinked has no access unless you grant it |
Get consent before a tool learns from it ABA Formal Opinion 512, Jul 2024For a self-learning tool, “a client’s informed consent is required prior to inputting information relating to the representation into such a GAI tool”. | Never learns from requests SIE serves fixed weights; a request never changes the model | Learns only what you train You fine-tune only on data you choose |
Use only the systems the client approves Outside counsel guidelinesA client’s own terms for the firms it hires; many name the systems and clouds a firm may use for that client’s data. | Moves when a client asks The same calls run in a client-approved cloud | Even air-gapped Mirrored images, telemetry off |
Start on SIE Serverless, or run it under your control
SIE Serverless
We host the models; you call one API.
- No idle GPUs, pay for what you use
- Fits your stack: SDK, API, CLI, MCP
- Self-host the same open-source engine
- SOC 2 Type 2, US or EU data residency
- Create an account to try Serverless
SIE Cluster
Kubernetes in your own cloud, backed by our engineers.
- Terraform modules for your cloud
- Apache-2.0, same engine as Serverless
- GPU workers scale to zero between jobs
- Separate worker pools for each tenant
Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.Deploy SIE to our Google Cloud 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 SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment. SIE Local
The open-source server on your own machine.
- Runs on your NVIDIA GPU
- One container, no cluster
- Process your data fully offline
- Same SDK and model IDs as Serverless
docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default Start from your task or your industry
Search and RAG
Documents
Text and LLM apps
- Chat Open LLM, OpenAI-style API
- Coding agents Tool calls for your coding agent
- Structured output Schema-valid JSON
- Classify text Your labels, no training
- Yes/no decisions Automate workflow checks
- Route requests Cheap or strong LLM, per request
- Extract names and dates People, orgs, places
- Knowledge graph Entities and their relations
- Translate text English into many languages