AI Readiness Assessment & Consulting
Turn AI goals into an approved, fundable plan.
Our AI readiness assessment scores your current state and ranks high-value use cases with budgets and ROI ranges to prove impact.
Our Offerings
AI Readiness Audit
We benchmark your current state across five pillars—data, platforms, applications, security/compliance, and team capability—using a scoring model. The audit maps data sources, quality, and access; catalogs systems and integrations and evaluates skills, processes, and budget. You get a quantified readiness scorecard, a gap list ranked by effort vs. impact, initial business AI readiness findings, and a starter backlog of feasible use cases.
AI Strategy & Prioritization
We turn goals into a ranked use-case portfolio using a clear scoring matrix: business value, feasibility, data availability, risk, cost, and time-to-impact. Each candidate includes problem statement, success metric, required data, stakeholders, integration points, and a funding range. The result is a decision-ready AI roadmap with near-term wins, cross-functional owners, and executive-level ROI scenarios.
Target Architecture Blueprint
We design a target architecture that fits your stack on AWS, Azure, or GCP: data pipelines, feature stores and vector databases, model gateways (commercial and open-source), prompt and policy layers, observability, and governance. Deliverables include an architecture diagram, build-vs-buy choices, security and audit controls, SLAs/SLOs, and a cost envelope to guide procurement and implementation.
RAG Pilot
We stand up retrieval-augmented generation for a focused knowledge domain (docs, tickets, policies, product data). Scope covers ingestion and chunking, embedding/model selection, guardrails, and an evaluation harness measuring answer accuracy, coverage, latency, and rejection quality. You leave with a working RAG service, side-by-side benchmarks against your baseline search, and a rollout plan tied to the target architecture.
Generative Assistant Pilot
We prototype an assistant for a defined workflow—support agent copilot, sales enablement, or internal knowledge chat. Work includes tool/action design, retrieval connectors, prompt and policy management, red-teaming, and telemetry. Deliverables are a usable assistant, usage and quality dashboards, and an operating playbook for scale-up across teams.
Predictive AI Pilot
We deliver a predictive model for a concrete decision: forecasting, classification, scoring, or anomaly detection. Steps include dataset assembly, feature pipeline, model training and backtesting, bias and privacy checks, and deployment packaging. You receive a model card, performance report against business KPIs, and an integration plan (batch or API) with your CRM/ERP or data warehouse.
MLOps & LLMOps
We establish the practices and tooling to run AI safely and repeatably: data and model versioning, prompt/version management, CI/CD for models and prompts, offline/online evaluation, cost and drift monitoring, incident response, and access controls. Outputs include pipelines as code, evaluation suites, dashboards, and runbooks that align with your governance requirements.
Managed AI Operations
We operate your AI services day-to-day: uptime and cost monitoring, periodic model and prompt updates, eval-driven quality reviews, access/audit management, and monthly steering on roadmap priorities. The engagement includes defined SLOs, compliance reporting, and a continuous improvement backlog so pilots progress into dependable production services.
Industries We Serve
- Retail & eCommerce
- Healthcare & Life Sciences
- Finance & Banking
- Logistics & Supply Chain
- Manufacturing
- Government & Public Sector
- Startups
- SaaS
- Telecommunications
- Education
Value We Deliver
Clear, decision-ready outcomes from AI readiness consulting you can act on within a quarter.
Want a decision-ready roadmap and pilot scope for your stack?
Why Choose WiserBrand
Practical advantages from AI readiness assessment and consulting delivered by one team. We bring regulated-industry fluency and reference architectures to turn decisions into shipped results.
1
Consulting + engineering in one team
Strategy, data, and ML engineers work as a single squad. We convert goals into a target architecture, build the pilot, and stand up MLOps/LLMOps with evaluation, monitoring, and runbooks. No handoffs that stall momentum.
2
Repeatable GenAI delivery
Deep practice in RAG, prompt and policy stacks, and assistant workflows. We ship six-week pilots with an evaluation harness for accuracy, latency, safety, and cost, then scale using reference patterns on AWS, Azure, or GCP.
3
Regulated industry fluency
US experience across finance, healthcare, retail, manufacturing, tech, and eCommerce. We align with GDPR/CCPA, HIPAA, and SOX/FINRA, implement access and audit controls, and provide board-ready artifacts with ROI scenarios and budget ranges.
Our Experts Team Up With Major Players
Partnering with forward-thinking companies, we deliver digital solutions that empower businesses to reach new heights.
Our Workflow
A five-step path from assessment to a shipped pilot, with clear decision gates and concrete artifacts.
Kickoff & Objectives
We align stakeholders on goals, constraints, and success criteria. Activities include a short discovery of business processes, systems and data sources, compliance boundaries, and a preliminary risk register.
Readiness Audit & Data Review
We run the AI readiness audit across data, platforms, applications, security and compliance, and team capability using a 0 to 5 scoring model. Work covers data inventory and quality checks, integration maps, plus an MLOps readiness check.
Use Case Portfolio & Business Case
We transform opportunities into a ranked portfolio using a scoring matrix for value, feasibility, data readiness, risk, cost, and time to impact. Each item includes a problem statement, metric to move, integration points, owners, dependencies, and a budget range.
Target Architecture & Pilot Planning
We define a target architecture that fits your stack on AWS, Azure, or GCP. Scope includes data pipelines, vector or feature stores, model gateways, prompt and policy layers, observability, and access control. In parallel we plan the pilot: success metrics, test datasets, offline and online eval design, acceptance criteria, and a detailed work plan.
Pilot Build, Evaluation & Go or No Go
We build a focused pilot such as RAG search, a generative assistant, or a predictive model. We track accuracy, coverage, latency, safety, and cost with side by side baselines. The outcome is a business review that includes results against KPIs, a productionization checklist, budget and timeline for rollout, and a governance and MLOps operating plan. If the decision is go, we schedule the production track; if not, we refine or pivot with clear next steps.
Client Success Stories
Explore how our services have helped businesses across industries solve complex challenges and achieve measurable results.
Frequently Asked Questions
Access to key stakeholders, read-only access to systems (data warehouse, CRM/ERP, help desk, cloud console), sample datasets, and current policies. We also ask for one process owner per target area to speed decisions.
Typically 2–4 weeks for the AI readiness assessment and MLOps readiness check, then a focused 6-week pilot. You get a go/no-go decision with KPI results at the end of the pilot.
A quantified AI readiness evaluation scorecard, a ranked roadmap with budgets and ROI ranges, a target architecture blueprint, a pilot PRD with an evaluation harness, and a governance packet (roles, decision gates, audit trail).
No. We work across AWS, Azure, GCP, and hybrid or legacy environments. Part of AI readiness consulting is mapping pragmatic upgrades that raise generative AI readiness without large upfront replatforming.
Typical ranges: pilots $30–75k; implementation $120–500k; managed operations $10–40k/month. Each item in the roadmap includes a budget band, owners, and an ROI hypothesis so you can sequence funding with confidence.