Private, self-hosted and on-premise AI for Australian firms — the regulatory drivers, the honest trade-offs, and how to decide what keeps your client data in-house.
AI for Lawyers - AI for law firms that keeps client matters privileged and on-shore
Contract review, legal research, document drafting, discovery and client intake — handled by Claude-powered agents configured so privileged material never leaks to a public model. Cloud-private with Australian data residency by default, fully self-hosted where the matter demands it. Founder-led, fixed price.
- Faster contract review
- 70–85%
- Throughput per lawyer
- 5x
- Kickoff to production
- 2–8 wk
- Data never leaves the firm
- Private
Where lawyers lose time today
- Contract and due-diligence review — associates reading hundreds of documents to extract terms and flag risk.
- Legal research — hours searching case law and statute, then synthesising a memo with verifiable citations.
- Document drafting — NDAs, engagement letters, and standard agreements drafted from scratch every time.
- Discovery — reviewing thousands of documents for relevance and privilege.
- Client intake and triage — assessing matter type, conflicts, and urgency before any legal work begins.
- The privilege problem — fee-earners pasting privileged material into public ChatGPT because the firm has no safe alternative.
AI workflows for lawyers - What we build for lawyers
Every engagement is fixed price and founder-led. We scope, ship, and support — typically 2–8 weeks from kickoff to production.
- Contract review & due diligence. AI reads contracts in any format, extracts key clauses (termination, liability, change of control, IP, indemnities), flags deviations from your standard positions, and produces a structured summary the lawyer reviews — instead of reading every page.
- Legal research assistant. Retrieval-augmented research grounded in actual legal databases — not the model’s memory — so findings are sourced and every citation is verifiable. It removes the initial research grind without inventing case law.
- Document drafting & automation. First-draft NDAs, engagement letters, and standard agreements generated from your templates and matter data. The lawyer refines and approves rather than starting from a blank page.
- E-discovery & privilege review. Classify documents by relevance, cluster by issue, and flag likely-privileged material for human confirmation — so reviewers focus on what matters instead of reading everything.
- Client intake & conflict triage. An agent gathers the key facts, identifies the matter type, runs a first-pass conflict check, and routes a structured brief to the right lawyer — with after-hours enquiries answered immediately.
- Private / on-prem deployment. The differentiator for legal: we configure a contractually-closed enterprise model (no training, no retention, no third-party disclosure) — or a fully self-hosted model — so privileged data never leaves the firm.
Why Sandlabs - Built for the duty of confidentiality, not just the demo
Legal AI lives or dies on privilege and confidentiality. We architect every engagement so privileged material stays protected, citations stay verifiable, and a qualified lawyer signs off on every output — not a generic AI chatbot bolted onto your practice.
- Privilege-safe by design. Privileged material is never sent to a public model or used for training. Cloud-private with no-retention terms, or fully self-hosted where the matter requires it.
- Australian data residency. Client data stays on-shore, aligned with the Privacy Act, the Australian Privacy Principles, and OAIC guidance on AI.
- Verification built in. RAG over real legal sources plus human-in-the-loop review on every output. AI assists; the lawyer signs off.
- Claude specialists. Long contracts and careful reasoning are exactly where Claude leads. We build on it every week.
- Founder-led, fixed price. Scoped, priced, and shipped — no open-ended hourly AI consulting.
- Ships in weeks. Most workflows go live in 2–8 weeks. Start with one (usually contract review), expand from proof.
- Can law firms use AI without waiving legal professional privilege?
- Yes, when it is configured correctly. The risk comes from pasting privileged material into public consumer AI tools like ChatGPT — courts and law societies have warned this can put privilege at risk. We deploy either a contractually-closed enterprise model (no training, no retention, no third-party disclosure) or a fully self-hosted model, so privileged content never leaves your control.
- Does the client data leave the firm?
- Not if you don’t want it to. For most firms a cloud-private enterprise model with Australian data residency and zero data retention is sufficient. For the most sensitive matters we deploy a self-hosted model that runs entirely inside your environment, so nothing transits a third-party provider at all.
- What legal work can AI actually do?
- Contract and due-diligence review, legal research with verifiable citations, first-draft document generation, e-discovery and privilege review, and client intake and triage. It removes the high-volume, repetitive grind that consumes associate time; lawyers keep the judgment work.
- Will AI hallucinate case law?
- It can, if it relies on the model’s training memory. We use retrieval-augmented generation (RAG) over actual legal databases so answers are grounded in real sources, with citations a lawyer can verify, and human review on every output before it is relied on.
- Do you build private or on-premise AI for law firms?
- Yes — it is a core part of what we do for legal. We deliver a "your data never leaves" setup, from contractually-closed enterprise AI through to fully self-hosted or on-premise models, chosen to fit the sensitivity of your matters. See our guide to private AI for Australian business for how the options compare.
- How much does AI for a law firm cost?
- A focused workflow (for example, contract review) runs $15K–$40K and ships in 2–4 weeks. A broader build covering multiple workflows runs $40K–$120K. Most firms start with a Discovery Sprint ($5K–$15K) that produces a fixed-price quote with ROI modelling before commitment. Ongoing support is typically $5K–$15K per month.
Further reading - Before you scope your AI project
The cost, scoping, and architecture questions we get most often — answered.
After the Fable 5 ban, cloud-AI dependency is a real business risk. What local LLMs are, the honest trade-offs, and a pragmatic hybrid strategy.
How IDP extracts data from invoices, contracts, and PDFs at 95%+ accuracy — architecture, AUD pricing, build-or-buy.
How AI consulting engagements are scoped, priced, and delivered — and the questions to ask before signing.
When off-the-shelf AI stops working and custom development starts to pay for itself.
15 AI automation platforms stress-tested in production by an Australian team — AUD pricing, data residency, honest verdicts.
Let's build something great together.
Melbourne, Australia — serving founders worldwide. [email protected]