Scalexa — Senior Engineering & AI Solutions
    United States

    AI consulting for US companies that need the system shipped

    You are not looking for a definition of AI — you are comparing vendors. We are a senior-only engineering team with 30+ years experience, working in US hours, billing weekly against hours actually worked, and judged on whether the system runs in production and holds up under audit.

    See our work
    Coverage
    Full US Eastern overlap, partial Pacific
    Assurance
    SOC 2-aligned practice, NDA before code access
    Team
    Senior-only, contracted and billed in USD

    How US teams engage us

    Four engagement shapes cover nearly every AI consulting conversation we have with US buyers. Each is fixed in scope, billed weekly, and ends with software your own engineers can run.

    • 2 weeks

      AI use-case and readiness assessment

      We review your data, systems and the workflow you want changed, then return the use cases worth funding, the ones that are not, and a build-and-run cost model your finance team can sign off.

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    • 6-16 weeks

      Production AI implementation

      RAG and LLM applications, forecasting, document extraction and computer vision built into your existing stack — with evaluation harnesses, guardrails, cost ceilings, observability and CI/CD your team keeps.

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    • Ongoing or project

      Machine learning engineering

      Feature pipelines, training and retraining, model serving and drift monitoring. The unglamorous half of ML that decides whether the model still works in month six.

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    • 1-3 weeks

      AI security, SOC 2 and assurance review

      Model, data and pipeline review against SOC 2-aligned practice and the enterprise security questionnaires your buyers send. Findings ranked by real exploitability, with a named senior reviewer on the report.

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    US work, and the conversation it usually starts

    Pick the one closest to your problem and book a call about that specific thing — you will speak to an engineer who worked on it, not an account manager.

    • Mozilla Foundation

      Global fundraising infrastructure for one of the best-known nonprofits in tech — high-traffic, payment-critical systems that could not fail during campaign peaks.

      Read the case study

    • Cityblock Health

      US healthcare data engineering: Amazon Redshift and Google Cloud consolidated into a BigQuery ETL platform built to carry ML and AI workloads on regulated health data.

      Read the case study

    • ListenLayer

      AWS DevOps and security for a US marketing data platform — 99.99% uptime and SOC 2 compliance, the bar most US enterprise buyers set before they sign.

      Read the case study

    • NavGen

      Computer vision over aerial imagery to detect roof damage and generate qualified leads — a US commercial AI product, not a pilot.

      Read the case study

    We have also built for Walmart and Porsche teams — browse all case studies.

    What US buyers search for, and where it lands

    See the US market page for the full keyword-to-service mapping, or the UK equivalent at AI consultancy UK.

    AI consulting — frequently asked questions

    What US buyers ask us before the first call.

    Most US engagements start with a two-week readiness assessment: we review your data, systems and the workflow you want changed, then return the use cases worth funding, the ones that are not, and a build-and-run cost model. From there, production builds typically run six to sixteen weeks and end with running software, evaluation harnesses and pipelines your own engineers own.

    We bill weekly on hours actually worked in USD rather than a padded fixed price. A scoped readiness assessment is the usual entry point; production implementations are sized after it, once the data and integration surface are known. You get the running cost of the system — inference, storage, monitoring — modeled before you commit to building it.

    Yes. Delivery runs with full US Eastern overlap and partial Pacific overlap, so stand-ups, reviews and incident calls sit inside your working day. Contracting and invoicing are in USD.

    We work to SOC 2-aligned engineering practice: least-privilege access, encryption in transit and at rest, audit logging, and NDAs signed before any code access. For AI systems we document what data is sent to model providers, what is retained and how retention is disabled — the answers your customers' security questionnaires ask for.

    In practice the labels overlap, and the useful question is who writes the production code. We do both halves: the advisory work that decides what to build, and the engineering that ships it. There is no handoff to a junior delivery team — the engineers who scope the work are the ones who build it.

    Retrieval-augmented search over internal documents, LLM-assisted support and back-office workflows, document extraction and classification, forecasting on operational data, and computer vision for inspection. The common thread is a measurable workflow with real data behind it, not a general-purpose chatbot.

    Frequently. We review the code, data pipelines, infrastructure and evaluation approach, then give you a direct read on whether to repair the current build or rebuild the parts that will not survive production load — with the reasoning, so you can disagree with it.

    Yes. Our US work includes healthcare data engineering for Cityblock Health and SOC 2-compliant AWS infrastructure for ListenLayer, alongside payment-critical fundraising infrastructure for the Mozilla Foundation. Regulated data changes the architecture, not our willingness to take the work.
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    Ready to Get Started?

    Book a free 30-minute discovery session with our senior engineers to identify quick wins and show you what's possible.

    View Our Work