Frequently Asked Questions

Most projects ship to production in 4-8 weeks. Simple automations can be live in 2-3 weeks. Complex RAG systems typically take 8-12 weeks. We'll give you a real timeline after our first call—not a sales estimate.

We integrate. Salesforce, HubSpot, Zendesk, that legacy Oracle system nobody wants to touch—we've connected to all of them. Our job is to make your existing stack smarter, not force you to rebuild everything.

Fixed-price for defined scopes. No hourly billing that punishes you for our efficiency. After our discovery call, you get a detailed proposal with transparent pricing. If the scope changes, we renegotiate before we bill.

Yes. We do private cloud, on-premise, air-gapped—whatever your security team needs. Your data stays in your VPC. We don't need access to production data to build the system.

We don't disappear. We offer maintenance packages, performance monitoring, and iterative improvements. AI systems get better with real usage data—we're there to make sure that happens.

You do. The IP we build for you is yours. We keep our proprietary frameworks and modules, but everything custom-built for your project belongs to you. No lock-in. No hostage situations.

We've been doing this long enough to set realistic expectations upfront. But if a project isn't going to deliver value, we'll tell you before we've burned your budget. We'd rather lose a project than ship something that doesn't work.

Synthara builds production-grade AI systems across four practice areas: agentic automation (multi-step autonomous workflows), RAG knowledge platforms (sovereign retrieval-augmented systems), machine learning solutions (custom predictive models and inference pipelines), and high-performance web applications powered by Next.js and React.

Yes. We specialise in sovereign AI deployments for healthcare (HIPAA), financial services (SOC 2, PCI-DSS), legal, and EU public sector. Every component — embedding models, vector store, retriever, LLM — runs inside infrastructure the customer legally controls, with no data egress to third-party tenants.

All major providers — OpenAI, Anthropic, Google Vertex AI, Mistral, AWS Bedrock, Azure OpenAI — plus self-hosted open-weight models (Llama 3.3, Mistral Large, Qwen) when sovereignty or cost requires it. Multi-provider routing with failover is a default in every production deployment.

Every system ships with a versioned evaluation harness: a 100–200 scenario golden set, an LLM-as-judge rubric scorer running in CI, production trace replay catching silent regressions, and a quarterly calibration against human-labeled samples. Without that harness you cannot ship prompt changes with confidence.

Generative Engine Optimization is the practice of structuring content so that AI answer engines — ChatGPT, Claude, Perplexity, Google AI Overviews — cite it when answering user questions. AEO is a synonym. Both are the AI-era counterpart to SEO, focused on becoming a source rather than a ranked result.

Yes. We audit existing AI products for citation extractability, structured data coverage, llms.txt and robots.txt posture, evidence density, E-E-A-T signals, and topical authority. Most existing products have material gaps that can be closed in 6–10 weeks.

Fixed-price engagements scoped after a discovery call. Most projects ship to production in 4–8 weeks. Simple automations can be live in 2–3 weeks. Complex multi-tenant RAG or agent systems typically take 8–12 weeks. We share a real timeline after the first call — not a sales estimate.

RAG is an architecture where an LLM generates answers grounded in retrieved documents instead of relying solely on its training data. A typical RAG system has four stages: embedding documents into a vector store, retrieving the most relevant passages for a query, optionally reranking them, and generating a citation-grounded answer with the retrieved evidence in context.

A chatbot follows a fixed conversational flow with limited tool use. An AI agent has an outer reasoning loop where the model decides what to do next — including calling tools, branching, looping, and self-correcting. Agents handle multi-step tasks (research, ticket triage, contract review) that chatbots cannot complete reliably.

Yes — see our projects page and our engineering blog. Many production deployments are under NDA, but we can usually share anonymised metrics and architecture diagrams on a discovery call.

You do. The IP we build for you is yours. We retain our proprietary frameworks and modules, but everything custom-built for your project belongs to you. No lock-in, no hostage situations.

Yes. We offer maintenance packages, performance monitoring, hallucination and groundedness sampling, and iterative improvements. AI systems improve with real usage data — we make sure that happens.