🇺🇸 SOC 2 Type II · HIPAA-ready on Bedrock · MCP-Native
Production AI Agents for US Companies — Built on Evals, Not Demos
Gartner predicts 40% of enterprise apps will use AI agents by 2026. We build the ones that ship: LangChain, CrewAI, AutoGen, Anthropic Claude, MCP, on AWS Bedrock with SOC 2 Type II and HIPAA-ready deployment. From $25K, 4-20 weeks, US-based PM.
The 2026 AI agent reality
- Enterprise apps using AI agents by 202640%
- Q1 2026 enterprise apps embedding an agent80%
- Average AI agent ROI171%
- Median payback period5.1 mo
Sources: Gartner, OneReach.ai 2026, Anthropic MCP registry. Full data below.
Six numbers that explain the budget shift. All sourced.
Enterprise apps using AI agents by 2026
Source: Gartner
Q1 2026 enterprise apps embedding an agent
Source: 120+ enterprise survey
Average AI agent ROI
Source: OneReach.ai 2026
Median payback period
Source: OneReach.ai 2026
Public MCP servers (April 2026)
Source: Anthropic registry
Forecast enterprise AI agent spend by 2027
Source: IDC + McKinsey
Six use cases with real payback windows. Not 50 hypothetical ones.
Customer Support Agent
3-6 monthsTier-1 deflection across email, chat, and voice. Reads your knowledge base, escalates with full context, opens tickets in your CRM. Typical: 50-70% deflection at week 8.
ExploreInventory & Demand Agent
5-9 monthsReads ERP + sales history, predicts low-stock, drafts POs, flags dead stock. Sits in Slack/Teams or your ops UI. Typical: 25-40% reduction in stockouts.
ExploreFinance & Invoice Agent
4-7 monthsInvoice OCR, three-way match, payment scheduling, anomaly detection. Reads QuickBooks/NetSuite/Odoo, writes to your AP system. Typical: 80% faster month-end.
ExploreSDR / Outbound Agent
3-5 monthsResearches accounts, drafts personalized outbound, books meetings in your CRM. Reads LinkedIn, company data, intent signals. Median payback: 3.4 months (industry data).
ExploreWorkflow Orchestration Agent
6-10 monthsMulti-step business processes — onboarding, KYC, RFP response — where the agent coordinates 5-10 tools and handoffs to humans on exception.
ExploreInternal Knowledge Agent
4-8 monthsRAG over Confluence, Notion, SharePoint, Drive. Slack/Teams interface. Citation-grounded answers, access-control aware. Typical: 30% drop in internal questions to senior staff.
ExploreEight tools we use, not 80 we name-drop. Choice depends on workload, not preference.
LangChain
Tool-use chains, RAG pipelines, fast prototyping.
CrewAI
Role-based multi-agent orchestration. Researcher → Writer → Reviewer flows.
AutoGen
Asynchronous agent workflows. Long-running tasks with human checkpoints.
Anthropic Claude SDK
Tool-use and computer-use. Our default for production agents.
Model Context Protocol (MCP)
Custom MCP servers for your CRM, ERP, and internal tools.
AWS Bedrock
Claude, Llama, Titan with HIPAA BAA and US data residency.
LangSmith / Langfuse
Eval harness, prompt versioning, production observability.
Pinecone / pgvector
Vector store choice depends on scale — we benchmark both.
Free AI Audit (48-hour turnaround)
A 30-min call plus a written assessment showing exactly where AI saves you time and money. No deck, no obligation.
Three engagement sizes. Pick the one that matches your scope.
Single-Purpose Agent
4-8 weeks · one workflow
- One agent, one job (support, SDR, FAQ, etc.)
- Anthropic Claude or AWS Bedrock
- 1-2 tool integrations
- Eval harness + prompt versioning
- 90-day post-launch tuning
- US-based PM
Multi-Tool RAG Agent
8-12 weeks · 5+ tools
- RAG over your docs (Confluence, Notion, S3)
- Custom MCP server for internal tools
- 5-10 tool integrations
- Production observability (LangSmith)
- Access-control aware retrieval
- 90-day SLA-backed support
Multi-Agent System
12-20 weeks · orchestrated
- 3+ specialized agents with orchestrator
- Human-in-the-loop checkpoints
- Custom evals + red-team harness
- SOC 2 / HIPAA / ITAR scope
- On-call rotation post-launch
- Roadmap planning quarterly
Most agents fail in production because evals were skipped. We build them first.
Use-case scoping (2 wk)
We map the workflow, the data sources, the tools, the success metrics, and the rollback plan. Fixed-fee discovery ($4,500). You leave with a written architecture — yours regardless of whether we build it.
Evals before prompts
The eval harness comes first. We build the test set with your domain experts, then write prompts to pass it. Most agent failures in production are eval-coverage failures, not model failures.
Single-agent build, weekly demos
Working agent in your sandbox by week 2. Weekly demo with real data. We instrument production observability (latency, cost, eval scores) from day one — not as Phase 2.
Tool-use and MCP integration
Connect to your CRM, ERP, ticketing, and data warehouse via MCP or function-calling. Custom MCP servers built where there is no public one. Access-control aware.
Production rollout with checkpoints
Shadow mode → 10% traffic → 50% → 100%. Every checkpoint has rollback criteria written in advance. If eval scores dip, we revert and investigate before promoting.
90-day SLA post-launch
Daily eval monitoring, weekly prompt tuning, monthly cost review. Most agents need real iteration in months 2-3 — we plan for it instead of treating it as scope creep.
Built for the compliance constraints your security team will ask about.
SOC 2 Type II
Annual audit. Role-based access, audit logs, encryption at rest, vendor management.
HIPAA-ready on AWS Bedrock
Signed BAA. PHI never leaves your account. De-identification at ingest if you want extra defense.
US data residency
US-East / US-West deployment. Zero-retention DPAs negotiated with Anthropic, OpenAI, AWS Bedrock.
Production observability
LangSmith or Langfuse for traces, costs, eval scores. PagerDuty for SLA breach.
Deeper into specific agent types
AI research worth reading before you build
The benchmarks, cost models, and framework comparisons we keep open in our own tabs.
Questions engineering leaders ask. Direct answers.
Skip the AI proof-of-concept graveyard.
Two-week paid architecture engagement ($4,500). At the end you have a written eval set, a scoped agent design, and an indicative quote.