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Entitybits
Services

Four things we ship.
Done well.

We don't do thirty things halfway. We focus on the three capabilities that make AI work in production — and we ship them end-to-end, from discovery to deployed system.

01AI Strategy & Advisory02RAG & Knowledge Systems03Agentic Workflows04AI Infrastructure
Service 01

AI Strategy & Advisory

From "we want to use AI" to a working system, governance model, and an internal team that can run it. Most enterprise AI programs stall before production. This is the work that prevents that.

What you get

  • AI opportunity mapping + prioritization (impact × feasibility)
  • Governance model — RACI, eval ownership, review cadences
  • Risk & policy controls — data classification, AUP, NIST AI RMF / ISO 42001 / EU AI Act mapping
  • AI readiness assessment + build/buy framework
  • Pilot-to-production roadmap (6–18 months)
  • Eval & measurement design — success in numbers, before launch
  • Architecture review · technical due diligence · vendor selection · AI feasibility study
  • Change management & first-90-days enablement
6–12 wks
Typical engagement
2–4 ppl
Team size
Start with strategy →See related work
STRATEGY.RUBRICLive
01OpportunityMap · Prioritize · Cut
02GovernanceRACI · Reviews · Audit
03RoadmapPilot → Production
04EvalsNumbers before launch
Service 02

RAG & Knowledge Systems

Production-grade retrieval, hybrid search, and grounded answers. Citations that actually link back. Latency budgets that hold under real load.

What you get

  • Hybrid retrieval pipeline (vector + keyword + reranker)
  • Document ingestion + chunking strategy
  • Eval harness for retrieval and answer quality
  • Production deployment with observability
  • Inline citation system back to source documents
4–8 wks
Typical engagement
3–5 ppl
Team size
12
Systems shipped
Start a RAG project →See related work
RAG.PIPELINELive
Query
→
Hybrid Retrieve
Rerank
→
Generate
Q: What's our refund policy for Pro tier?
Pro tier refunds are issued within 14 days of purchase if < 5% of credits used. [policies/refunds.md§2]
Service 03

Agentic Workflows

Multi-step agents with tool use, planning, and eval-driven iteration. Built to be debugged in production — not demoed in a sandbox.

What you get

  • Agent architecture (single-agent or multi-agent)
  • Tool definitions + secure execution layer
  • Planner + verifier loops with eval gates
  • Trace logging + step-by-step debugging UI
  • Failure-mode playbook + recovery patterns
6–12 wks
Typical engagement
4–6 ppl
Team size
7
Systems shipped
Start an agent project →See related work
AGENT.RUNTIMELive
INPUTUser intentparsed + classified
PLANTool selection3 tools chosen
EXECUTERun + collect2.1s · 4 steps
VERIFYEval gate97% pass · ✓ ship
Service 04

AI Infrastructure

Deployment, observability, and cost controls that turn AI features into real products — and keep them that way as scale and model versions change.

What you get

  • Model gateway with provider failover + fallback
  • Per-tenant rate limits + cost budgets
  • End-to-end tracing (request → tools → tokens → cost)
  • Eval pipeline with regression alerts
  • Runbook + on-call playbook
3–6 wks
Typical engagement
2–4 ppl
Team size
4
Systems shipped
Start an infra project →See related work
RUNTIME.OBSERVABILITYLive
4.7sp95
$0.04/req
97%eval
Last 24h · cost / req↘ -12%
How we engage

Four engagement models. Pick what fits the work, not the slide.

  • Strategy & Advisory

    AI roadmap, governance, and architecture for organizations starting from strategy.

  • Product Development

    End-to-end product engineering with AI built in from day one. We own the build.

  • AI Implementation

    Scoped AI systems shipped to production with eval gates — RAG, agents, document intelligence.

  • Team Augmentation

    Senior leadership embedded with your team. Interim CTO, VP Engineering, or Head of AI.

We work across fixed-price, time-and-materials, and dedicated-team models. Pricing is discussed live, not in a document.

Technology Stack · 2026

What we're actively shipping with this year.

  • Languages
    PythonTypeScriptNode.jsGoJavaSQLDart
  • Frontend
    ReactNext.jsReact NativeFlutterStreamlitTailwind
  • Backend
    FastAPIExpressNestJSFastifySpringGo
  • LLMs (frontier)
    Claude Opus 4.7Claude Sonnet 4.6Claude Haiku 4.5GPT-5Gemini 3Llama 4
  • AI Frameworks
    LangChainLangGraphLlamaIndexCrew AICustom orchestration
  • Vector Databases
    PineconeQdrantOpenSearchFAISSWeaviate
  • Cloud & AI Platforms
    AWS BedrockAWS SageMakerGCP Vertex AIAzure OpenAI
  • Databases
    PostgreSQLMySQLDynamoDBMongoDBColumnar stores
  • Data Engineering
    Apache AirflowDagsterdbtPandasNumPy
  • DevOps
    DockerKubernetesGitHub ActionsGitLab CITerraform
  • Real-time & IoT
    WebSocketMQTTWebRTCSIPFCMAPNs
  • AdTech Integrations
    XandrOpenXPubMaticIndex ExchangeMedia.netEquativ60+ networks

We pick by workload, not by trend. Vector DB, model, framework, cloud — chosen with the client, documented, and moved on from.

How we work

Discovery to deployed system in four phases.

No twelve-week "AI strategy" decks. We ship working systems and iterate against real evals.

— 01

Discovery

Define the system, the eval set, and the success criteria. Pre-mortem the failure modes you'll see in production.

1–2 weeks
— 02

Build

Implement the system end-to-end. Daily eval runs against the discovery set. Weekly demos against your team.

2–8 weeks
— 03

Harden

Cost, latency, reliability. Load tests. Failure injection. Cost ceiling enforced. Runbook + on-call playbook.

1–2 weeks
— 04

Operate

Optional: we keep running it. Or we hand off with the eval, observability, and incident playbook your team needs.

Ongoing

Have a real AI system to ship?

30-minute scoping call. We'll tell you if it's worth doing — and if not, what to do instead.

Start the conversation →