Services

Production AI services, grouped the way you actually need them.

Strategic guidance to figure out what to build. Implementation to ship the system. Infrastructure to run it reliably. Pick the slice you need — or take it all.

Fixed price, fixed scope Deployed in your cloud Code in your repo Senior engineers only
Strategic Guidance

Where to invest, what to build, how to ship.

Cut through AI complexity with senior, vendor-neutral guidance. We help you decide what to build, what to buy, and what to skip — before a line of code is written.

AI Strategy

Fractional CTO-level guidance for AI roadmaps that hit ROI.

We help you turn AI ambition into a 90-day execution plan. Build vs. buy decisions, model choice, infrastructure footprint, and a phased roadmap optimized for measurable ROI rather than headlines.

What you get

  • Written 90-day roadmap with clear milestones
  • Build vs. buy recommendations per use case
  • Reference architecture and rough budget
  • Risk register and mitigation plan

Typical timeline

1–2 weeks

Tech we use

Vendor-neutral
AI Implementation

Production systems, not slideware.

We build the AI systems that actually ship. Every implementation is fixed-price and fixed-scope, with a working pilot in your hands in 3–4 weeks.

AI Agents

Production-ready agents with tool use, observability, and integrations.

Agents that actually work in production — not just in demos. We build with proper tool calling, observability, evaluation, and graceful failure modes. Integrated into Slack, internal APIs, and the rest of your stack.

What you get

  • Multi-step agent with tool integration
  • Observability dashboard with traces
  • Evaluation suite for behavior regression
  • Auto-scaling deployment in your cloud

Typical timeline

3–4 weeks pilot · 6–10 weeks production

Tech we use

AWS BedrockAzureOpenAIAnthropic

RAG & Embeddings

Conversational answers grounded in your private data.

Retrieval-augmented generation done right: evaluated retrieval, hybrid search, citation traceability, and an evaluation harness so you know your answers stay accurate as your data changes.

What you get

  • Hybrid retrieval (semantic + keyword)
  • Citation-grounded answers with source links
  • Evaluation suite with accuracy metrics
  • Incremental ingestion pipeline for fresh data

Typical timeline

3–4 weeks pilot · 6–8 weeks production

Tech we use

PineconeQdrantpgvectorOpenSearch

Document Processing

Extract structured data from messy, unstructured documents.

Contracts, invoices, forms, claims — we build pipelines that parse, classify, and extract structured data from documents at scale, with confidence scoring and human-in-the-loop fallbacks for the edge cases.

What you get

  • End-to-end OCR + LLM extraction pipeline
  • Confidence scoring and HITL workflow
  • Structured output to your DB or API
  • Throughput tuning for production volume

Typical timeline

3–6 weeks

Tech we use

AWS TextractAzure Document IntelligenceGCP DocAI

Fine-Tuning & Inference

Domain-specific models that improve accuracy and cut inference costs.

When prompting and RAG aren't enough, fine-tuning produces smaller, faster, cheaper, more accurate models for your specific domain. We handle data prep, training, evaluation, and deployment.

What you get

  • Curated training set with eval splits
  • Fine-tuned model on your benchmark
  • Cost / quality / latency comparison
  • Production-ready inference endpoint

Typical timeline

3–6 weeks

Tech we use

AWS SageMakerAWS BedrockAzure MLopen-source
AI Infrastructure

Run it in your cloud, your way.

AI infrastructure that's secure, observable, and cost-controlled. We work in your cloud account so you keep full ownership of your data, models, and spend.

Cloud Migration

Move from third-party APIs to your own cloud account.

Migrate from OpenAI / Anthropic API dependencies to models running in your own AWS, Azure, or GCP account. Cut per-call costs, keep your data inside your VPC, and gain full control over models, versions, and capacity.

What you get

  • Equivalent (or better) accuracy on your evals
  • Per-call cost reduction breakdown
  • Models running inside your VPC
  • Cutover plan with safe rollback

Typical timeline

4–8 weeks

Tech we use

AWS BedrockAzure OpenAIGCP Vertex AI

DevOps & MLOps

Production-grade CI/CD, monitoring, and infra for AI workloads.

Production AI is a systems problem, not a model problem. We set up CI/CD, IaC (Terraform/CDK), eval pipelines, monitoring, alerting, IAM, and auto-scaling — all tuned for the spiky cost and latency curves of AI workloads.

What you get

  • Infra-as-code for full reproducibility
  • CI/CD with automated evals as gates
  • Cost dashboards and budget alerts
  • Observability with traces and quality metrics

Typical timeline

2–4 weeks

Tech we use

AWSAzureGCP

Have a use case in mind? Let's scope it together.

Book a free 60–90 minute strategy session — you leave with a written architecture recommendation and rough budget.