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Dreamztech is an AWS Partner, Google Cloud Partner and Microsoft Solutions Partner with senior architects holding AWS Solutions Architect Professional, Azure Solutions Architect Expert, Google Cloud Architect, AWS / Azure / Google ML Specialty and security certifications — plus 100+ AI agent engagements delivered across 15 countries since 2012.
Most enterprise AI agent failures happen before a line of code is written — wrong use case, wrong topology, wrong LLM choice, wrong framework, brittle integration plan, no eval strategy, no governance. AI agent consulting and development is what prevents that — senior advisory that grounds your AI agent program in measurable business value and a reference architecture you can actually ship and operate.
We deliver consulting that ends with code — readiness assessment, use-case discovery, ROI modelling, build-vs-buy analysis, reference architecture, vendor selection, fixed-scope pilots and production delivery on AWS, Azure or Google Cloud. HIPAA-eligible, SOC 2 Type II, ISO 27001 / 27018 and NIST AI RMF / EU AI Act aligned from day one.
Quick Answer: AI agent consulting and development is end-to-end advisory plus delivery — readiness assessment, use-case discovery, ROI modelling, build-vs-buy analysis, reference architecture design, LLM and framework vendor selection (GPT-4o vs Claude vs Llama, LangGraph vs CrewAI vs AutoGen), NIST AI RMF / EU AI Act compliance scoping, fixed-scope pilots and full production delivery — all from the same senior team.
DreamzTech’s AI agent consulting and development engagements start at $15,000 (2-week strategy and readiness assessment with reference architecture deliverable) up to $400,000+ (12-week consulting-led production build with multi-agent system, custom integrations, compliance documentation and managed ops handoff). All HIPAA-eligible, SOC 2 Type II, ISO 27001 / 27018 and FedRAMP-aligned on AWS, Azure or Google Cloud.
Reviewed by the DreamzTech AI Strategy Practice — Reviewed and updated 2026-05-12. Includes hands-on guidance from senior AI agent architects, certified AWS / Microsoft / Google Cloud consultants and 100+ enterprise engagements.
Six tightly-scoped service tracks — AI agent readiness assessment, use-case discovery & ROI modelling, reference architecture & vendor selection, build-vs-buy analysis, fixed-scope pilots, and consulting-led production development.
2–3 week senior-led assessment of your data, integrations, team, compliance posture and AI-ops maturity. Delivered as a written readiness report with named gaps, recommended fills and a phased adoption roadmap.
Senior-led workshops to identify, prioritise and ROI-quantify candidate AI agent use cases. Volume × hours × hourly-cost modelling with payback timelines per workflow.
Reference architecture design — LLM choice (GPT-4o vs Claude 3.5 vs Llama 3.3 vs Gemini), framework choice (LangGraph vs CrewAI vs AutoGen), hosting choice (AWS Bedrock vs Azure OpenAI vs GCP Vertex), integration patterns and observability stack — with cost / latency / governance trade-offs documented.
Honest, vendor-agnostic comparison of SaaS agent platforms (Sierra, Decagon, Cognigy, Moveworks), hyperscaler agent APIs (Bedrock Agents, Azure AI Agents, OpenAI Assistants) and custom-built agents — with named trade-offs on cost, lock-in, customisation depth and compliance fit.
Production-grade 4–8 week pilots built on the recommended reference architecture — single workflow, 2–3 integrations, eval harness, observability, guardrails and managed-ops handoff. Real users, real data, real ROI before scaling.
Full end-to-end production AI agent development on the reference architecture from the consulting phase — multi-agent systems, deep CRM/ERP integration, eval-driven engineering, compliance documentation and managed-ops handoff. Same senior team end-to-end.
AI agent consulting and development services are the right fit when you have business pressure for AI agents but uncertainty on use cases, ROI, vendor choice, build-vs-buy or compliance — or when you have a build planned but want senior architectural review before committing.
Senior consulting before delivery transforms AI agent ROI. Across DreamzTech’s 100+ engagements customers see 3–6 month faster time-to-first-production-agent vs in-house ramp, 50–70% lower wasted spend on dead-end LLM / framework choices, 2–4× higher pilot success rate vs unscoped builds, and zero compliance-blocking findings on SOX / HIPAA / GDPR / EU AI Act audits because governance is scoped from day one.
Every engagement follows a six-layer consulting-led methodology — readiness, discovery, architecture, governance, pilot delivery and production scale-up. Scales from 2-week strategy sprints to 14-week consulting-led production builds.
Data, integration, team, compliance and cloud architecture readiness audit — what you have, what you need to fill before AI agents can succeed.
Use-case discovery workshops, ROI modelling with volume × hours × cost analysis, prioritisation matrix and phased adoption plan.
Reference architecture design with LLM, framework, hosting, integration and observability choices — with cost, latency, governance trade-offs.
NIST AI RMF, EU AI Act, HIPAA, SOC 2, GDPR compliance scoping. System cards, model cards, intended-use and audit-log architecture from day one.
Fixed-scope 4–8 week production pilot on the reference architecture — real users, real data, real ROI, full eval harness, guardrails and observability.
End-to-end production development from the proven pilot — multi-agent systems, deep CRM/ERP integration, compliance documentation, managed-ops handoff.
Buyers often compare specialist AI agent consulting and development against pure-play strategy consultancies (McKinsey QuantumBlack, BCG GAMMA), Big-4 (Deloitte, EY, KPMG, PwC) and generalist system integrators. This section makes the distinction crisp.
| Engagement | Duration | Fixed Scope | Best For |
|---|---|---|---|
| Readiness Assessment | 2 weeks | $15K–$25K | "We want AI agents — where do we start?" |
| Reference Architecture | 4 weeks | $30K–$60K | "We know the use case — which LLM, framework, hosting?" |
| Architecture Review | 2–4 weeks | $20K–$45K | "We built a pilot — review it before we scale" |
| Consulting-Led Pilot | 8 weeks | $75K–$150K | "We want a working production pilot with same-team continuity" |
| End-to-End Consulting + Development | 12–14 weeks | $200K–$400K+ | "Strategy through production — same senior team end-to-end" |
| Fractional AI Architect | Ongoing | $8K–$30K / month | "In-house team building — we want senior architectural oversight" |
Our AI agent consulting and development services span 8 high-stakes industries — from healthcare HIPAA-eligible programs to BFSI SOX-audit-ready strategy, legal CLM agent advisory, insurance claims automation and public sector FedRAMP-aligned engagements.
HIPAA-eligible AI agent strategy, prior-auth and clinical-document use-case discovery, Epic / Cerner / FHIR integration architecture and named-author EEAT compliance scoping.
Claims-triage and fraud-detection strategy, FNOL automation roadmap, Guidewire / Duck Creek integration architecture, ACORD-form-aware multi-agent design.
M&A due-diligence and contract-intelligence strategy, iManage / NetDocuments integration architecture, fine-tuned legal NER advisory, EU AI Act high-risk scoping.
AP automation, KYC/AML and lending-decision strategy, SAP / Oracle / Dynamics 365 integration architecture, SOX audit-trail design from day one.
AWS GovCloud / Azure Government strategy, FedRAMP and IL5 scoping, permit / benefits / FOIA workflow advisory and procurement support.
Customer-service, recommendation and inventory agent strategy, Shopify / Magento / SAP Commerce integration architecture and seasonal capacity planning.
Shop-floor copilot strategy, predictive-maintenance roadmap, SAP / Oracle / MES integration architecture and 21 CFR Part 11 audit advisory.
Onboarding, employee self-service and recruiter agent strategy; Workday / BambooHR / SuccessFactors integration architecture; data-privacy advisory.
You're reading our AI Agent Consulting and Development page (strategy + advisory + delivery). Already have a plan and need build only? See LLM Agent Development or Multi-Agent AI Systems. Need ongoing ops? See Managed AI Agent Services.
Bring your toughest AI agent question — "where do we start?", "should we buy or build?", "Claude or GPT-4o for our domain?", "LangGraph or CrewAI?", "how do we stay EU AI Act compliant?" — and a senior AI strategy architect will walk you through the recommended engagement, deliverables and budget. Live, on the call. Free, 30 minutes, no obligation.
AWS Partner, Google Cloud Partner and Microsoft Solutions Partner. AWS Solutions Architect Professional, Azure Solutions Architect Expert, Google Cloud Architect plus AWS / Azure / Google ML Specialty and security certifications. 100+ AI agent engagements across 15 countries since 2012 — every project ends in shippable architecture or shipped code.
Tell us your AI agent question, your current maturity and your target outcome. A senior strategy architect will reply within one business day with a recommended engagement (readiness assessment, use-case discovery, reference architecture, build-vs-buy analysis or consulting-led pilot), deliverable list, timeline and fixed-scope budget. No sales pitch, no obligation.
Explore how DreamzTech has advised and delivered AI agent strategy, reference architectures and production pilots for Fortune 500 enterprises and high-growth mid-market — from 2-week strategy sprints to 14-week consulting-led production builds.
A Fortune 500 enterprise SaaS company engaged DreamzTech for a 3-week AI agent strategy and readiness assessment. We audited 12 candidate use cases, ROI-modelled each, prioritised customer-support deflection and sales-qualification as 90-day pilots, recommended Claude 3.5 Sonnet + LangGraph + Salesforce MCP servers as reference architecture, and identified $1.4M of wasted spend the in-house team was about to commit to a dead-end Cognigy + custom Lambda stack. Result: 62% faster time-to-production, $1.4M wasted spend avoided, and a phased 12-use-case roadmap with named sponsors and milestones.
A global retail bank engaged DreamzTech for a 4-week AI agent reference architecture and vendor selection engagement before committing to its multi-agent ITSM platform build. We benchmarked Claude 3.5 vs GPT-4o vs Llama 3.3 on representative tickets, compared LangGraph vs CrewAI vs AutoGen for the planned hierarchical topology, evaluated AWS Bedrock vs Azure OpenAI hosting, designed the ServiceNow MCP server pattern, and produced a written reference architecture with cost / latency / governance trade-offs. Result: 70% build risk reduction, 8 architecture iterations saved, 18 weeks faster to first production pilot.
A national P&C insurance carrier engaged DreamzTech for a 2-week strategy sprint followed by an 8-week consulting-led production pilot of a fraud-detection multi-agent system. Same senior team end-to-end. Phase 1: readiness assessment + reference architecture + LLM vendor selection (Claude 3.5 Sonnet vision + GPT-4o reasoner). Phase 2: production pilot build with Guidewire MCP integration, eval harness against 50K historical claims and EU AI Act high-risk documentation. Year 1: 62% fraud catch rate lift, $5.1M prevented losses, 8 weeks to first production agent.
AWS Partner, Google Cloud Partner and Microsoft Solutions Partner. Senior architects with deep AI agent build experience — not strategy slide-deck consultants. Every consulting engagement ends in a shippable reference architecture or shipped production pilot. 100+ engagements across 15 countries since 2012.
A structured, transparent four-phase process designed for senior AI agent consulting that flows into shippable delivery — from readiness assessment to production pilot, with named-architect accountability throughout.
Senior-led 1–3 week audit of data, integrations, team, compliance and cloud architecture readiness. Written report with named gaps, recommended fills and prioritised adoption roadmap.
Use-case discovery workshops, ROI modelling, LLM and framework benchmarking on your representative data, build-vs-buy analysis, reference architecture with cost / latency / governance trade-offs and NIST AI RMF / EU AI Act compliance scoping.
Fixed-scope 4–8 week production pilot on the reference architecture — real users, real data, real ROI measurement. Full eval harness, observability and guardrails. Same senior team that wrote the strategy.
Consulting-led production build — multi-agent systems, deep CRM/ERP integration, compliance documentation, managed-services handoff or in-house knowledge transfer. Same team end-to-end with no outsourcing handoffs.
AWS Partner, Google Cloud Partner and Microsoft Solutions Partner-grade AI agent consulting — every engagement includes compliance scoping (NIST AI RMF, EU AI Act, HIPAA, SOC 2, GDPR), governance documentation and audit-log architecture designed in from day one.
Every consulting engagement scopes NIST AI Risk Management Framework documentation requirements upfront — system cards, model cards, intended-use and prohibited-use statements, evaluation results plans and continuous-monitoring frameworks. By the time code ships, your compliance documentation is ready, not bolted on after audit findings.
For EU deployments we scope EU AI Act conformity assessment for limited-risk and high-risk AI agent classifications — risk management system, data governance, technical documentation, record-keeping, transparency obligations, human oversight, accuracy / robustness / cybersecurity requirements and post-market monitoring plan.
Every engagement maps applicable regulatory requirements per industry — HIPAA Privacy and Security Rules with BAA chains for healthcare; SOX Sec 404 ITGC controls for financial services; GDPR Article 22 (automated decision-making) and Article 30 (records of processing) for EU; SOC 2 Type II controls for SaaS; 21 CFR Part 11 for pharma. All built into the reference architecture, not retrofitted.
Reference architectures pass AWS / Azure / Google Cloud Well-Architected Framework reviews across all pillars — Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization and Sustainability. Documented evidence packets delivered for your cloud-team review.
Governance scoping includes responsible AI principles, ethical guardrail design, audit log architecture (per-prompt, per-tool-call, per-approval), bias and fairness evaluation plans, transparency obligations and human oversight workflows. Built into the reference architecture from day one.
Consulting engagements include honest vendor-agnostic TCO analysis — SaaS agent platform licensing, hyperscaler agent API costs, custom-build engineering costs, ongoing managed-ops costs over 3 years. Vendor lock-in risk per option and recommended mitigation strategies. Procurement support if needed.
Information security
BAA across all major clouds
Responsible-AI documentation
Annual audit certified
Conformity assessment
ADA-accessible UI
Built on the AWS / Azure / Google Cloud Well-Architected Frameworks plus deep AI agent build experience across every major LLM, framework, hosting platform and enterprise integration target.
Real feedback from CIOs, Chief AI Officers and VPs of Engineering whose AI agent strategies, reference architectures and production pilots were delivered by DreamzTech.
Every AI agent consulting and development engagement at DreamzTech is led by senior architects who advise and build — not strategy consultants who hand off to outsourcers. Methodology grounded in NIST AI RMF, EU AI Act, AWS / Azure / Google Cloud Well-Architected Frameworks, eval-driven engineering and HIPAA-eligible, SOC 2 Type II, ISO 27001-aligned delivery.
Deep build experience across LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, Anthropic Claude, OpenAI GPT-4o, Llama 3.3, Gemini 2.0 and Amazon Titan — bridged to your enterprise tools via Model Context Protocol. Same senior team from readiness assessment through production pilot and managed-services handoff.
Pick the engagement that fits your AI agent maturity — from rapid 2-week strategy sprints to 14-week consulting-led production builds.
$15,000–$25,000 fixed. 2-week senior-led readiness assessment, use-case discovery and ROI modelling. Deliverable: written readiness report, prioritised use-case portfolio, named gap-fill recommendations, phased adoption roadmap.
$30,000–$60,000 fixed. 4-week LLM benchmarking, framework selection, hosting decision, integration pattern selection, observability stack design and compliance scoping. Deliverable: written reference architecture with cost / latency / governance trade-offs.
$75,000–$150,000 fixed. 2-week strategy + 6-week production pilot — single workflow, 2–3 integrations, full eval harness, observability, guardrails, real users and real data. Deliverable: production pilot in your environment.
$200,000–$400,000+ fixed-scope. Strategy through production — readiness, discovery, architecture, multi-agent build, CRM/ERP integration, eval harness, compliance documentation and managed-services handoff. Same senior team end-to-end.
Readiness assessment, use-case discovery, ROI modelling, reference architecture design, LLM and framework vendor selection, build-vs-buy analysis, fixed-scope pilots and consulting-led production development — all from the same senior team end-to-end on AWS, Azure or Google Cloud.
Four real options exist for AI agent strategy: (1) Pure-play strategy consultancies (McKinsey QuantumBlack, BCG GAMMA, Bain Helix); (2) Big-4 advisory (Deloitte AI, EY, KPMG, PwC); (3) Generalist system integrators with AI add-ons (Accenture, Infosys, TCS, Wipro); or (4) Specialist AI agent consulting and development firms like DreamzTech who advise and build with the same senior team. Here’s the honest comparison.
| Capability | Strategy Consultancies (McKinsey / BCG / Bain) | Big-4 (Deloitte / EY / KPMG / PwC) | Generalist SIs (Accenture / Infosys / TCS) | DreamzTech AI Agent Consulting and Development |
|---|---|---|---|---|
| Architects Who Build | Slide-deck consultants | Hand off to outsourcers | Some build, broad scope | Senior architects with 100+ shipped agents — advise and build |
| Same Team End-to-End | No — strategy only | No — handoff | Sometimes | Yes — readiness through production cutover |
| Vendor-Agnostic LLM Selection | Variable | Often partner-preferred | Often partner-preferred | Honest benchmark on your data — Claude, GPT, Llama, Gemini, Titan |
| Time to First Production Agent | 9–18 months | 6–12 months | 6–10 months | 8–14 weeks |
| Cost (12-week engagement) | $1.5M–$3M+ | $800K–$2M | $500K–$1.2M | $200K–$400K fixed-scope |
| Deliverable | Strategy slides | Strategy + handoff doc | Plan + scope SOW | Reference architecture plus shipped production pilot |
| Best For | Board-level AI strategy | Enterprise AI program design | Broad-scope multi-year transformations | Production AI agents shipped in 8–14 weeks with senior architectural depth |
When DreamzTech’s AI agent consulting and development is the right call: when you want senior architects who advise and then build (not strategy-only firms who hand off to outsourcers); when you need named accountability across the full lifecycle (readiness → architecture → pilot → production → managed ops); when generalist SIs lack the LLM / framework / agent-topology depth your build needs; or when you need a fixed-scope deliverable at one-third the rate of Big-4 advisory. Most clients hit their first production agent in 8–14 weeks vs 9–18 months with strategy-only consultancies.
Common questions from CIOs, Chief AI Officers, VPs of Engineering and AI program leads evaluating senior AI agent consulting and development engagements.
AI agent consulting and development is end-to-end advisory plus delivery — readiness assessment, use-case discovery, ROI modelling, build-vs-buy analysis, reference architecture design, LLM and framework vendor selection (GPT-4o vs Claude vs Llama vs Gemini; LangGraph vs CrewAI vs AutoGen), NIST AI RMF / EU AI Act compliance scoping, fixed-scope pilots and full production delivery — all from the same senior team. DreamzTech engineers advise and build, eliminating the strategy-to-delivery handoff that drives most enterprise AI agent failures.
Most enterprise AI agent failures happen because the strategy consultants who design the program hand off to outsourcers who build something different. We end that handoff. The senior architect who runs your readiness assessment is the same architect who designs your reference architecture, leads your pilot build and signs off on production cutover. Same accountability end-to-end. Same context throughout. No translation loss.
Four phases — the DreamzTech ADVISE Framework: (1) Assess — readiness audit of data, integrations, team, compliance and cloud architecture. (2) Architect — use-case discovery, ROI modelling and reference architecture (LLM, framework, hosting, integration, observability). (3) Pilot — fixed-scope 4–8 week production pilot. (4) Scale — consulting-led end-to-end production build with managed-ops handoff. Engagements range from 2-week strategy sprints to 14-week end-to-end builds.
2-week strategy sprint: $15K–$25K fixed. 4-week reference architecture engagement: $30K–$60K fixed. 8-week consulting-led pilot: $75K–$150K fixed. 12-week end-to-end consulting + development: $200K–$400K+ fixed-scope. Custom enterprise engagements available for FedRAMP / IL5 / HIPAA-covered programs. All scopes are written and fixed before engagement starts.
Three differences. (1) Architects who build — senior engineers with 100+ shipped AI agents advise, not slide-deck consultants. (2) No handoff — same team from readiness through production. (3) Vendor-agnostic — we benchmark Claude vs GPT vs Llama on your data, recommend LangGraph or CrewAI per use case, not “our preferred partner.” Most clients pay 30–50% of Big-4 rates for engagements that ship working code at the end.
2-week readiness assessment — fastest path to a written report and prioritised roadmap. 4-week reference architecture — strategy plus LLM benchmarking plus framework selection plus compliance scoping. 8-week consulting-led pilot — strategy plus production pilot in your environment. 12-week end-to-end — strategy through production cutover. Custom 14–22 week engagements available for enterprise multi-agent platforms.
Yes — every engagement. We benchmark Claude 3.5 Sonnet, GPT-4o, Llama 3.3 70B, Gemini 2.0 and Amazon Titan on your representative data. We compare LangGraph, CrewAI, AutoGen, LlamaIndex, Bedrock Agents, Azure AI Agents and OpenAI Assistants API against your use-case fit, latency budget, governance requirements and team skills. Our recommendation is documented with named trade-offs — you can disagree and we will document why.
A 2–3 week senior-led audit of: (1) Data readiness — quality, volume, labelling, governance for the candidate use cases. (2) Integration readiness — CRM / ERP / ITSM authentication models, RBAC, audit log fitness. (3) Team readiness — engineering, prompt-ops, SRE skills gap. (4) Compliance readiness — HIPAA, SOC 2, NIST AI RMF, EU AI Act posture. (5) Cloud architecture readiness — Well-Architected gaps. Deliverable: written report with named gap-fill recommendations.
Cross-functional workshops (5–8 senior stakeholders) over 1–2 weeks. We identify 10–20 candidate use cases, score each on impact, feasibility, risk, time-to-value, then ROI-model the top 5–8 with volume × hours × hourly-cost analysis and payback timeline. Output: prioritisation matrix, recommended 2–3 pilot use cases with named sponsors, sensitivity-tested ROI projections and phased adoption plan.
A reference architecture is the technical blueprint your build team works from — LLM choice (with benchmark data), framework choice (with trade-offs documented), hosting choice (Bedrock vs Azure OpenAI vs Vertex), integration patterns (MCP servers vs native SDK), observability stack (LangSmith / Langfuse / Arize), evaluation strategy and governance documentation requirements. Without a written reference architecture, build teams burn 6–12 weeks on iteration; with one, they hit first production pilot in 4–8 weeks.
Yes — honest, vendor-agnostic comparison. SaaS agent platforms (Sierra, Decagon, Cognigy, Moveworks, Forethought) for fast standard vertical use cases. Hyperscaler agent APIs (Bedrock Agents, Azure AI Agents, OpenAI Assistants) for cloud-aligned simple agents. Custom builds on LangGraph / CrewAI / AutoGen for cross-vendor LLM routing, deep CRM/ERP integration, regulated industries, or unique domain knowledge. We model 3-year TCO and lock-in risk for each option per use case.
Every consulting engagement scopes compliance from day one. NIST AI RMF — system cards, model cards, intended-use, evaluation plan, monitoring. EU AI Act — risk classification, technical documentation, transparency, human oversight, post-market monitoring for limited-risk and high-risk classifications. HIPAA — BAA chain across cloud services. SOX 404 ITGC — change control, segregation of duties, audit logging. GDPR — Article 22 (automated decisions), Article 30 (RoP). Built into the reference architecture, not retrofitted.
A 4–8 week production pilot — same senior team that wrote the strategy and reference architecture also builds the pilot. Single workflow, 2–3 integrations, full eval harness, observability and guardrails. Real users, real data, real ROI measured in production. Deliverable: working pilot in your environment with documentation, knowledge transfer and either managed-services handoff or in-house enablement.
Yes — and we do this honestly. We have evaluated Sierra, Decagon, Cognigy, Moveworks, Forethought, Ada, Voiceflow against custom builds for customer-support workflows. Glean, Microsoft Copilot, Notion AI for enterprise search. AWS Bedrock Agents, Azure AI Agents, OpenAI Assistants, Google ADK for hyperscaler-native agents. We pick what is best for your use case, even if it means recommending against custom development.
Yes. 2–4 week senior-architect-led review of an existing or planned AI agent build — LLM choice, framework choice, integration patterns, observability gaps, eval strategy, compliance posture and production-readiness. Deliverable: written architecture review with named risks, recommended fixes and prioritised remediation plan. Common engagement when a client built a PoC and wants senior review before scaling.
This service covers the full lifecycle — readiness through production — with consulting depth at every stage. LLM Agent Development is build-only (you arrive with a clear plan). Multi-Agent AI System Development is build-only for multi-agent topologies. AI Agent Integration Services is integration-only. Managed AI Agent Services is operations-only (Day 2+). AI Agent Consulting and Development wraps strategy around build — pick this when you have uncertainty.
Yes — for SaaS agent platforms and LLM vendor contracts. We provide reference TCO models, vendor lock-in risk analysis, recommended SLA structures, data-retention contract clauses, indemnification fitness review and benchmark pricing comparisons. We do not take vendor commissions — recommendations are based purely on your use-case fit. Particularly valuable when buying $100K+ enterprise SaaS platforms or 3-year commit deals.
Yes — across 5 dimensions: (1) Data & integration maturity — quality, governance, accessibility; (2) Use-case maturity — clarity, prioritisation, ROI rigour; (3) Build maturity — eval discipline, observability, guardrails; (4) Operations maturity — SRE, prompt-ops, model upgrades; (5) Governance maturity — NIST AI RMF, EU AI Act, compliance audit-readiness. Scored 1–5 per dimension with phased uplift roadmap.
Eight primary verticals — Healthcare (HIPAA prior-auth, clinical Q&A), BFSI (SOX-compliant AP, KYC/AML, lending), Legal (M&A due diligence, contract intelligence), Insurance (claims, fraud, underwriting), Retail (customer service, recommendation), Manufacturing (shop-floor, supplier QA), Public Sector (FedRAMP / GovCloud / IL5) and HR/Talent (onboarding, employee self-service). Each engagement includes industry-specific compliance scoping.
Embedded in every engagement. We facilitate cross-functional workshops (engineering, ops, compliance, business), document RACI for the pilot, identify named executive sponsor and named technical lead per use case, model change-impact and recommend communication / training plans. For large engagements we deliver an executive summary deck and a 12-month adoption roadmap with quarterly milestones.
Three paths — your choice. (1) In-house build — we hand off the reference architecture, your team builds (we are available for ad-hoc architecture consultations). (2) Consulting-led production build — same senior team continues into 8–14 week production delivery. (3) Managed services — production pilot moves to Bronze / Silver / Gold managed AI agent services tier. Engagements often combine paths 2 and 3.
Yes — for AI agent programs. Fractional senior architect engagements (4–16 hours / week) for clients building in-house but wanting senior architectural oversight, quarterly reviews, vendor evaluations and crisis support. Common for mid-market enterprises pre-hiring their first Chief AI Officer or post-strategy-engagement clients wanting continuity. Pricing: $8K–$30K / month based on hours and scope.
Yes. 2-week fixed-scope architecture review for an existing or planned AI agent build — senior architect dives deep into your LLM choice, framework, integrations, observability, eval and compliance posture. Deliverable: written architecture review (15–25 pages) with risks, recommended changes and prioritised remediation. Pricing: $20K–$35K fixed. Most clients use this before committing to a 3–6 month build.
Book a free 30-minute strategy architect call. Bring your AI agent question — “where do we start”, “should we buy or build”, “which LLM and framework”, “how do we stay EU AI Act compliant”, “review our pilot architecture” — and a senior architect will recommend the right engagement (readiness assessment / architecture review / consulting-led pilot / end-to-end), deliverables and a fixed budget. Written proposal within 1 business day. No sales pitch, no obligation.