Tell us which workflow you want to improve: an AI agent, a RAG knowledge system, a product integration, or a data pipeline. Start with the problem and what a useful result would look like.
Walk through one workflow, identify the systems and constraints, and discuss a practical next step. No project brief required.
This introductory call is separate from a paid Discovery Sprint. Any further work is scoped and agreed afterward.
DataBackfill LLC
California, United States
We follow the sun
A short introduction is enough. Project details are optional. Please do not include credentials, confidential documents, or personal data about other people.
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DataBackfill is a boutique AI & data delivery studio. We build production AI agents, RAG and knowledge systems, Claude integrations, and data infrastructure, including GA4-to-BigQuery pipelines, for companies that need working software, not slide decks.
Our focus is hands-on implementation: connecting models and data to an operating workflow. Discuss the intended outcome, integrations, evaluation criteria, and handover responsibilities with us so the proposed work has a clear boundary.
Yes. We design and deploy production Claude implementations integrated with your existing stack, including prompt engineering, evals, guardrails, and tool use for agentic workflows.
Yes. We build retrieval and knowledge systems over proprietary sources. Relevant documents do not guarantee a correct answer: source grounding, document permissions, evaluation, and a fallback when evidence is missing belong in the project scope.
Yes. Data infrastructure and GA4-to-BigQuery pipelines remain part of our services, alongside DataBackfill Sync. For an existing product or account issue, include a non-sensitive description of the problem or use the support email on this page. Do not send account credentials.
Timing and pricing depend on the agreed workflow, integration access, data readiness, testing, and deployment requirements. Share the outcome you need and any target date or budget constraints. Scope, exclusions, delivery dates, and ongoing support should be confirmed in a proposal rather than inferred from a general estimate.
Describe who uses the workflow, which systems it touches, and how you would judge a useful result. Note where a person must approve or take over, and whether your team can provide access and review outputs. A clear problem is more useful for an initial discussion than a finished technical specification.