Debt Recovery Litigation LLM
Budget: £250 – £750 GBP
I’m looking for an AI engineer who can design and train a domain-specific large language model that operates like an in-house debt-recovery litigation team for medium-sized businesses. The core capabilities I need it to perform are clear:
• Chasing overdue invoices with context-aware, tone-appropriate correspondence
• Generating fully-formed claim packs and issuing court papers
• Drafting robust defences and persuasive witness statements ready for filing
I want the model to sit behind an API so clients can plug it straight into their existing CRM or finance platform. You are free to build on any strong open-source foundation model (e.g., Llama-2, GPT-J, Falcon) and fine-tune with reinforcement learning, retrieval-augmented generation or other techniques that will boost legal accuracy and citation fidelity. Keeping costs under control through smart parameter sizing and efficient inference is important; latency must stay acceptable for real-time user chats.
Because the advice produced carries legal weight, I will require:
1. Transparent data-sourcing and cleaning methodology
2. A validation suite that stress-tests the model against realistic debt-recovery scenarios, checking for statutory compliance and hallucination rates
3. Explainable output options (chain-of-thought hiding toggled off by default but available for audit)
4. Documentation covering deployment, versioning and safe-use guidelines for non-technical staff
There’s no rigid deadline—quality and reliability matter more than speed—so we can set milestones around dataset curation, prototype inference, closed beta and final release. If you bring experience with Hugging Face pipelines, LangChain orchestration, vector databases, or similar tooling, that will accelerate the process.
Let’s discuss your proposed architecture, dataset plan and an estimated training roadmap.
• Chasing overdue invoices with context-aware, tone-appropriate correspondence
• Generating fully-formed claim packs and issuing court papers
• Drafting robust defences and persuasive witness statements ready for filing
I want the model to sit behind an API so clients can plug it straight into their existing CRM or finance platform. You are free to build on any strong open-source foundation model (e.g., Llama-2, GPT-J, Falcon) and fine-tune with reinforcement learning, retrieval-augmented generation or other techniques that will boost legal accuracy and citation fidelity. Keeping costs under control through smart parameter sizing and efficient inference is important; latency must stay acceptable for real-time user chats.
Because the advice produced carries legal weight, I will require:
1. Transparent data-sourcing and cleaning methodology
2. A validation suite that stress-tests the model against realistic debt-recovery scenarios, checking for statutory compliance and hallucination rates
3. Explainable output options (chain-of-thought hiding toggled off by default but available for audit)
4. Documentation covering deployment, versioning and safe-use guidelines for non-technical staff
There’s no rigid deadline—quality and reliability matter more than speed—so we can set milestones around dataset curation, prototype inference, closed beta and final release. If you bring experience with Hugging Face pipelines, LangChain orchestration, vector databases, or similar tooling, that will accelerate the process.
Let’s discuss your proposed architecture, dataset plan and an estimated training roadmap.