AI Automation
AI Automation for Real Estate and B2B Operations
AI automation uses machine learning and large language models to take over repetitive work such as reading documents, answering routine questions, qualifying leads or searching large datasets in plain language.
It is for real estate and B2B teams with a clear, repetitive process that costs staff hours today, and with data they are allowed to use.
Peregrine scopes each project to that business problem, builds the model or LLM pipeline, and wires it into your existing systems with human review where accuracy matters.
At a glance
AI Automation at a Glance
| What's delivered | Natural-language search, lead qualification assistants, document extraction, predictive models and workflow automation, with human review |
|---|---|
| Engagement model | Fixed-scope projects, monthly retainers, or a combination, agreed after the discovery call |
| How it starts | A 30-minute technical discovery call with an engineer |
| Pricing | Pricing is scoped per project after the discovery call. |
| Related case studies | AI-Powered Multi-Market Real Estate Platform; Insurance Claims Automation for Claims Processing; Recruitment ATS for Applicant Tracking & Hiring |
Fit
Is AI Automation the Right Fit?
A good fit when
- A specific, repeated task consumes staff time: reading documents, answering the same questions, qualifying leads or searching records.
- You have historical data or documents for that task and can say what a correct result looks like.
- A person can review the uncertain cases, so the automation does not have to be right every time.
Probably not the right fit when
- The goal is to "add AI" with no workflow chosen and no measure of success. Pick the problem first.
- Every result must be correct with no human review, in a domain where an error is costly. Design for review or do not automate that step.
- There is no data to test against and none can be collected.
What we build
What Does Peregrine's AI Automation Service Include?
Peregrine's AI automation service covers natural-language search over your own data, lead qualification assistants, document extraction from PDFs and scans, predictive models such as valuation or scoring, and workflow automation with n8n, Zapier or custom services. Each project targets one business problem and keeps human review where accuracy matters.
Natural-language search
Search that understands requests written the way people talk, parses them into structured filters and returns ranked results from your own data, whether listings, products or documents.
Lead qualification assistants
Chat assistants that respond to inbound leads immediately, ask qualifying questions and route qualified conversations to the right person in your CRM.
Document extraction
OCR and extraction pipelines that turn PDFs, scans and forms into structured records, with validation rules and confidence thresholds that send uncertain cases to a person.
Predictive models
Valuation, scoring and forecasting models trained on your historical data, with the evaluation set and error metrics agreed before training.
Workflow automation
Automations with n8n, Zapier or custom services that connect AI steps with your existing tools, replacing manual hand-offs.
Industries: Real Estate & Proptech · Healthcare & Insurance · HR, Payroll & Recruitment · All industries
Timeline
How Long Does an AI Automation Project Take?
Peregrine does not commit to an AI automation timeline before a prototype. We first pick one workflow and agree what accuracy is good enough, then run a short prototype on a sample of your data to show whether the approach works. The production build follows only once the prototype proves out, in two-week sprints.
Pick the problem
We identify one workflow, measure how it performs today, and agree what "good enough" accuracy means before any model is chosen.
Prototype on real data
A short prototype on a sample of your data shows whether the approach works, before a full build is committed.
Build with human review
Production pipeline with confidence thresholds, review queues and logging so every automated decision can be traced.
Measure and tune
After launch we track accuracy and exceptions and retrain or adjust prompts as the data changes.
Scope
What Determines the Scope of an AI Automation Project?
These are the inputs that change the effort. None needs a final answer before the first call, but each one you can answer makes the estimate tighter.
- Task and accuracy target
- What "good enough" means and what an error costs. A higher target means more evaluation, more review tooling and more iteration.
- Data
- Volume, format and quality of the inputs, and whether examples with known correct answers exist to measure against.
- Approach
- A hosted language model, retrieval over your own documents, a trained model or plain rules differ in build effort and in running cost.
- Human review
- Confidence thresholds, a review queue and a log of every automated decision are a large part of a production build.
- Where results go
- Writing results into a CRM, claims system or other tool is integration work on top of the model.
- Data restrictions
- Whether data may be sent to a third-party model provider, and rules for personal or health data, narrow the options.
- Running cost
- Model providers charge per request, so cost scales with volume and is paid to the provider, not to the builder.
A sensible first phase
A sensible first phase is a short prototype on a sample of your own data, measured against an accuracy target agreed beforehand. It tells you whether the approach works before a production build is committed.
Cost
What Does AI Automation Cost?
Peregrine works on AI automation as fixed-scope projects, monthly retainers, or a combination, agreed after the discovery call and, for most work, after a short prototype on your own data. Cost depends on the workflow, the data available and how much human review the process needs. We do not publish a price list.
Getting started
How Does an AI Automation Project Start?
A Peregrine AI Automation project starts with a 30-minute technical discovery call with an engineer, not a salesperson. Larger builds then begin with a discovery sprint that maps requirements, designs the architecture and produces a written technical plan. Smaller, well-defined tasks can use the quick project form for a scoped estimate.
What to bring to the scoping call
- One workflow described step by step, and how long it takes today.
- A sample of real inputs, anonymized if needed, with the correct outcome for each.
- What a wrong result costs, and who would review the uncertain ones.
- The systems the result has to be written to.
- What data may and may not leave your environment.
- Volume: items per day.
Partial answers are fine. Whatever is missing becomes the first item on the call.
Case studies
Which Case Studies Show Peregrine's AI Automation Work?
Three published case studies show Peregrine's AI automation work: the W3|re real estate platform with conversational property search, an automated valuation model and a lead qualification chatbot; an insurance claims platform that extracts documents with AWS Textract and routes uncertain cases to people; and an ATS with resume parsing and candidate search.
AI-Powered Multi-Market Real Estate Platform
Conversational property search with a fine-tuned LLM, an automated valuation model, and a lead qualification chatbot for a multi-market brokerage.
Read the case studyInsurance Claims Automation for Claims Processing
Claims document extraction with AWS Textract, custom validators and confidence thresholds that route uncertain extractions to human review.
Read the case studyRecruitment ATS for Applicant Tracking & Hiring
Resume parsing that extracts structured candidate data from PDFs and Word files and makes it searchable with Elasticsearch.
Read the case studyStack
Technology We Use
Related services
Services Often Scoped With AI Automation
FAQ
Frequently Asked Questions
Where does AI automation actually help?
It helps most in high-volume, repetitive work with a clear right answer: extracting fields from documents, answering routine questions, sorting and qualifying leads, or turning plain-language requests into database queries. It helps least where decisions are rare, high-stakes and hard to define.
How do you prevent an AI system from making things up?
We ground answers in your own data through retrieval, constrain outputs to structured formats, validate them against business rules, and send low-confidence results to a person. Every automated decision is logged so it can be reviewed.
Will our data be used to train public models?
It should not be. The major LLM API providers offer business terms that exclude customer data from training, and models can also be hosted in your own cloud account. Which option fits is agreed, in writing, before the project starts.
Do we need a lot of data to start?
For LLM-based search, assistants and document extraction, you can often start with the data you already have. Predictive models such as valuation or scoring need enough historical examples, which we check during the prototype.
Can AI features be added to our existing software?
Yes. Most of our AI work plugs into an existing CRM, website or internal tool through its API rather than replacing it.
Who owns the IP rights to what Peregrine builds?
You do. Once the work is paid for, the client owns the intellectual property (IP) rights to what Peregrine builds for the project, whether it is delivered as a fixed-scope project, a monthly retainer or a combination of the two.
Questions about the team, contract models and how progress is shown are answered on the services page.
Technical consultation
Talk to an Engineer About AI Automation
Tell us what you are building or fixing. Your first conversation is with an engineer, not a salesperson.
- A 30-minute technical discovery call with an engineer, not a salesperson.
- Fixed-scope projects, monthly retainers, or a combination, agreed after the call.
- Small, well-defined tasks get a scoped estimate. We reply within 1 business day.
Prefer to pick a time? Book a call on Calendly
