Dashhold

AI Feature Engineering

AI feature engineering for B2B SaaS — production-grade, not demo-grade

Production AI features inside B2B SaaS — LLM integrations, RAG pipelines, AI-powered operator workflows, and prompt-engineering systems that hold up under real production load.

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Capabilities

What we ship

AI features inside the products our customers already trust. Not chatbots bolted onto landing pages — real workflows that change how operators and customers work.

  • LLM-powered features inside existing SaaS products
  • RAG pipelines over private data with embeddings and vector search
  • AI-powered operator workflows: triage, summarization, classification
  • Prompt-engineering systems with versioning, evals, and rollback
  • Cost and latency engineering: caching, model routing, batching
  • Safety and observability: PII redaction, output evaluators, audit logs
Production patterns

The patterns AI features need to hold up

Production AI is mostly engineering, not prompt engineering. The features that survive are the ones built on patterns that handle latency, cost, eval, and safety.

  • Retrieval-Augmented Generation over private data with embeddings and vector search

  • Operator-side AI: triage, summarization, classification, drafting

  • Customer-side AI: smart suggestions, search, in-app assistants with strict scoping

  • Multi-model routing: cheap models for the easy 80%, frontier models for the hard 20%

  • Eval-driven prompt engineering with versioning and rollback

  • Output safety: structured generation, evaluators, PII redaction, audit logs

Technology stack

A production AI stack, not a demo

We pick the layer the engagement actually needs. Bedrock for regulated stacks. OpenAI and Anthropic where speed matters most. Self-hosted Llama 3 when sovereignty is the constraint.

Models & APIs

  • OpenAI
  • Anthropic Claude
  • Gemini
  • Mistral
  • Llama 3
  • Bedrock

Orchestration

  • LangChain
  • LlamaIndex
  • Inngest
  • Temporal
  • Cube

Vector & retrieval

  • pgvector
  • Pinecone
  • Weaviate
  • Qdrant
  • Turbopuffer
  • Vespa

Evaluation & observability

  • Braintrust
  • LangSmith
  • Helicone
  • Phoenix
  • OpenTelemetry

Outcomes

Outcomes from production AI builds

AI features earn their keep when they change real workflow metrics, not when they ship a flashy demo. These are the outcomes we hold ourselves to.

  • Operator decision time cut by 40-70% on triage and classification surfaces
  • p95 generation latency under 1.5s on customer-facing AI features
  • Eval coverage on every prompt change so model upgrades ship without regressions
  • Per-tenant cost ceilings with model-routing keeping usage within budget
  • Audit-grade output logging that survives regulator review for regulated platforms
Selected AI engineering work

AI features in production

New AI engineering case studies are publishing soon.

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AI engineering FAQ

What teams ask before shipping AI features

Are you adding chatbots, or shipping AI features?
AI features. We do not bolt chatbots onto landing pages. We build LLM-powered workflows inside the products you already ship — operator triage, customer-side suggestions, RAG over private data, AI-powered classification — with eval coverage, cost ceilings, and audit logging from day one.
Which models do you build on?
Whichever the engagement actually needs. OpenAI and Anthropic for most production features. Bedrock for AWS-native regulated stacks. Self-hosted Llama 3 or Mistral when sovereignty or cost demand it. We architect around a model-routing layer so swapping providers later is a config change, not a rewrite.
How do you keep AI feature costs under control?
Three patterns. Model routing sends the easy 80% of traffic to cheaper models and reserves frontier models for the hard 20%. Aggressive caching at the prompt-fingerprint layer catches repeated requests. Per-tenant cost ceilings with structured fallbacks keep usage inside budget even when demand spikes. We instrument cost-per-feature from day one so the team can see what each AI surface actually costs.
How do you handle AI safety and compliance for regulated platforms?
PII redaction at the boundary. Structured generation with output evaluators that fail closed. Audit logging on every input and output, retained per the regulator's policy. For regulated platforms we route through Bedrock or Azure OpenAI with the regulator-approved data-handling agreements, never the public APIs. Safety is a design input, not a final review.
Further reading

Field guides on AI feature engineering

Background on the engineering decisions behind every AI feature build.

Let's build it together

Adding AI to a product you already ship?

A 30-minute call on the workflow you're trying to change, the data available, and what an honest first AI feature looks like.

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