Real estate
Listing copy in every language
Property descriptions generated and translated from structured listing data and photos, reviewed by an agent before they go live.
AI Solutions
Custom AI solutions that hold up in production
We design, build and run AI copilots, retrieval-augmented assistants and document-intelligence pipelines on leading LLMs — grounded in your data, measured against your own test cases, and embedded in the tools your team already uses.
copilot · legal-assistant
Can the tenant terminate the lease early, and with how much notice?
Yes — after the first 12 months, with 6 months’ written notice[1]. The addendum waives the penalty for relocation abroad[2].
Ask about any contract in the archive…
Sources
- [1]lease-agreement-2024.pdfp. 4 · §7.2
- [2]addendum-03.pdfp. 1 · §2
Guardrails passed
- Citations
- 2 / 2
- Eval score
- 94%
- Latency
- 1.4 s
Sound familiar?
When a demo isn't enough
- 01
Your AI pilot impressed in a demo but gives inconsistent answers on real data.
- 02
Teams copy-paste between documents, inboxes and systems to answer the same questions every day.
- 03
Sensitive data can't leave your cloud tenant, so off-the-shelf chatbots are off the table.
- 04
You need to know what every AI answer costs, and whether it's right, before rolling it out.
Capabilities
What we build with large language models
Production AI features, not prototypes — with retrieval, guardrails and observability designed in from day one.
AI copilots & assistants
Role-specific assistants that draft, answer and act inside your product, CRM or intranet, with permissions that mirror your users'.
Retrieval-augmented generation (RAG)
Hybrid search over documents, tickets and knowledge bases, with a source citation on every answer so people can verify it.
Document & data extraction
Contracts, invoices, IDs and PDFs turned into validated, structured data — with confidence scores and human review for edge cases.
AI agents & tool use
Agents that call your APIs to look things up, fill in records and trigger workflows — limited to explicit actions, with a full audit trail.
Content & media generation
On-brand copy, product descriptions, translations and image enhancement at scale, following your tone of voice and approval rules.
Evaluation & guardrails
Test sets built from your real cases, automated scoring, prompt-injection defenses and PII redaction — quality measured, not assumed.
Use cases
AI use cases we deliver
Customer support
Answers grounded in your knowledge base
An assistant that resolves repetitive tickets with cited answers and hands off to a person, with full context, when it isn't sure.
Finance & operations
Invoice and contract intake
Parties, amounts, dates and clauses extracted from incoming documents into your ERP, with low-confidence fields routed for review.
Sales
A copilot inside your CRM
Account summaries, follow-up drafts and call notes logged automatically — in the CRM your sales team already works in.
Process
How we take AI from prototype to production
- 01
Use-case & data audit
We rank candidate use cases by value and feasibility, and check the data each one depends on.
Deliverable: Prioritized use-case shortlist
- 02
Evaluation set first
Before writing prompts, we collect real examples and agree on what a correct answer looks like.
Deliverable: Test set & success metrics
- 03
Build & iterate
Retrieval, prompts, tools and UI, iterated against the evaluation set until quality and cost hit target.
Deliverable: Working pilot with scorecard
- 04
Harden & run
Guardrails, monitoring, cost controls and feedback loops, then a staged rollout to your users.
Deliverable: Production release & dashboard
Technology
Model-agnostic by design
- Models
- Anthropic ClaudeOpenAI GPTAzure OpenAIGoogle GeminiOpen-weight models
- Retrieval
- PostgreSQL + pgvectorAzure AI SearchHybrid search & re-ranking
- Runtime
- TypeScript & PythonAzure & Google CloudTracing & evaluation
Which LLM should we use — Claude, GPT or an open-source model?
It depends on the task, latency, cost and where your data has to stay. We're model-agnostic: we benchmark candidate models on your own evaluation set, pick the one that meets the quality bar at the lowest cost, and keep the architecture ready to switch as models improve.
Will our data be used to train AI models?
No. We use commercial API terms under which providers don't train on your inputs, and we can deploy through Azure OpenAI or other services inside your own cloud tenant and region, including the EU.
How do you stop the AI from making things up?
By grounding answers in retrieved sources with citations, constraining outputs to strict schemas, and testing against an evaluation set of real cases before and after every change. When confidence is low, the system says so or hands off to a person.
How long does a first AI project take?
It depends on scope and data readiness. We start with a narrowly scoped pilot on one high-value use case, measured against agreed metrics, so you see results early and decide on the rollout with real numbers.
Can AI work with our existing software?
Yes. Assistants and agents connect to your CRM, ERP, document storage and internal APIs while respecting existing user permissions. When a system has no API, we build the connector.
AI Solutions
Scope your first AI use case
Share the process or product you want to make smarter. We'll come back with a concrete use case, the data it needs and how we'd measure success.