Sageflow Labs

Services

Services for product, AI, backend, cloud, and data.

First releases, AI features in live environments, Spring Boot systems, cloud moves, and data platforms — scoped as fixed projects or ongoing retainers.

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Common project shapes

First-version product

A usable release in weeks: APIs, auth, data model, and a deploy path your team can demo to customers or investors.

AI in production

LLM features, agents, and RAG that run under load — with monitoring, spend control, and a path to operate after we leave.

Steady-state engineering

Upgrades, patches, incidents, and performance for systems that already make money — without hiring a full platform team.

Move or modernize

Cloud cutovers, Spring Boot upgrades, and data-platform changes with a written plan finance and security can review.

Product & MVP

  • Rapid MVP development

    End-to-end MVP: backend, APIs, auth, cloud deploy, and handoff docs. Built to launch, not as throwaway prototype code.

  • AI-native product MVP

    Ship an AI-first product quickly — chat, copilots, or workflow automation with real backends, eval hooks, and production deploy paths.

  • Product engineering squad

    Embed with your product team for roadmap delivery — features, integrations, and quality gates on a fixed or retainer model.

AI & Intelligent Systems

  • AI-forward deployment

    Production deployment of LLM apps and agents: model gateways, scaling, secrets, observability, rollback, and environment promotion — AI-ready from day one.

  • LLM platform engineering

    Shared AI infrastructure for product teams — routing, rate limits, caching, multi-provider failover, and cost controls across apps.

  • RAG & agent systems

    Retrieval pipelines, tool-using agents, and orchestration designed for reliability — not demos that break under real traffic.

  • Vector search & embeddings infrastructure

    Embedding pipelines, vector stores, hybrid search, and indexing ops so RAG and semantic features stay fast and maintainable.

  • AI evaluation, safety & quality ops

    Offline/online evals, regression checks, red-team basics, and quality gates before and after you ship model or prompt changes.

  • AI / LLM FinOps

    Token and infra spend baselines, waste maps, budgets, and guardrails — optional integration with TokenSentinel and your cloud billing.

  • MLOps & model serving

    Model packaging, serving (including GPU where needed), versioning, canaries, and monitoring for classical ML and foundation-model workloads.

Backend

  • Java Spring Boot development

    APIs, domain services, and integrations for fintech, healthcare, and real estate — secure, testable, production-grade.

  • Spring Boot maintenance

    Ongoing care for live systems: Spring/JDK upgrades, security patches, performance, and knowledge transfer.

  • Legacy → Spring Boot migration

    Modernize aging Java stacks with phased cutovers — less risk than a big-bang rewrite.

  • Event-driven & real-time backends

    Kafka/Pulsar-style pipelines, webhooks, and low-latency APIs for notifications, payments, and operational workflows.

  • API platform & integration layer

    Gateway design, partner APIs, B2B integrations, and versioning so product and partners move without breaking each other.

Cloud & DevOps

  • Cloud migration

    Move workloads to AWS, GCP, or Azure with architecture, cost baselines, and runbooks your team can own.

  • Kubernetes & delivery

    Clusters, CI/CD, environments, and reliable releases — including managed ops when you want us to run the platform.

  • Platform engineering (IDP)

    Internal developer platforms, golden paths, and self-serve deploy templates so product teams ship without ticket hell.

  • Serverless & edge architectures

    Lambda/Cloud Functions, edge runtimes, and CDN-aware designs for global latency and cost-efficient scaling.

  • Cloud operations & FinOps

    Reliability, cost control, backups, and operational hygiene for product and AI workloads.

  • Observability & SRE modernization

    Metrics, tracing, logs, SLOs, and on-call practices that make incidents shorter and releases safer.

  • App security & zero-trust foundations

    Secrets, IAM, network boundaries, and secure CI for products that handle money, health, or personal data.

Data & Analytics

  • Snowflake

    Warehouse design, roles, cost-aware compute, and data products your analysts can trust.

  • ETL / ELT pipelines

    Ingestion, transforms, quality checks, and orchestration — batch or near real-time.

  • Databricks & lakehouse

    Lakehouse setup, jobs, governance, and production pipelines on Databricks.

  • Streaming & real-time analytics

    Event streams to dashboards and decisions — Flink/Spark Streaming, CDC, and operational metrics that stay fresh.

  • Data governance & quality

    Catalogs, lineage, access policies, and tests so regulated teams can use data without losing control.

Need something on this list?

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