AI built to survive production, not just demo well

Yes, from day one, in a repo you control. Not after final payment, not after a retention period. And ownership only counts if it's portable: you get your data in standard, exportable formats and a standard vector store, so owning it actually means something.

You can hire anyone. We deliberately use open frameworks (Python/Node), standard vector databases, and documented decisions. AI can generate large volumes of unmaintainable code fast, which makes lock-in risk higher unless someone actively prevents it, so preventing it is part of the job.

We reduce and detect it; we won't claim to eliminate it, because no honest provider can. Grounding in your content, citation validation, output guardrails, and a standing eval suite are how we keep fabrication low and catch it when it happens. Anyone promising zero hallucinations is selling you the demo.

Fixed-price discovery first, then time-and-materials delivery. Discovery is time-boxed to define requirements and map complexity and integrations. T&M is the honest model for AI because the real work depends on what discovery uncovers; pure fixed-price forces either padding or corner-cutting. Build costs scale with corpus size, and there is a recurring monthly run cost.

AI has an opex tail that a static site doesn't: run cost is dominated by LLM API tokens. Depending on corpus size and traffic, monthly run typically ranges from the low hundreds to several thousand dollars. We engineer caching, model routing, and compression specifically to keep that controlled, and you see the usage live in Aphra.

A well-scoped proof of concept is roughly 4 weeks with clean data, longer with legacy systems or messy data. First meaningful, evaluated results land in about 45 to 60 days. Full production for a mid-size org is typically 3 to 6 months; complex, multi-source, compliance-heavy systems take longer. Data preparation is usually 30 to 50 percent of the effort and the usual reason timelines slip, so we say that up front.

Seniors own the architecture, retrieval design, guardrails, the eval suite, integration decisions, and everything that ships. AI accelerates the grunt work: ingesting and chunking documents, drafting the pipeline and eval cases, and monitoring cost and quality. AI never goes live unsupervised, and it never makes a decision that reaches your customer without a human reviewing it.

The evaluation suite is the answer. Without a golden dataset and baseline metrics you're guessing, and silent regressions ship whenever someone tweaks a prompt. We build a versioned set of normal, edge, and adversarial cases, gate every change on it, and evaluate retrieval, guardrails, and each prompt in isolation so we can localize any failure.

We're explicit about what's sent to model providers, retention, and confidentiality, and we put a data-processing agreement in place. PII is filtered at the guardrail layer, retrieved content is treated as data and never as executable instructions, and multi-tenant isolation prevents one tenant's documents leaking into another's, a real and common failure in naive setups.

You keep everything: full access to the code, documentation, environments, and the eval suite, plus a data export in portable formats. Because we built on open frameworks and standard stores, there's no wall to hit and no ransom to pay to walk away. That's the point of owning it from day one.