The strongest AI consulting proposal is not automatically the lowest-priced, longest, or most technically fashionable. It is the proposal that proves how a business decision will become a safe, measurable and operable system. Before scoring, reject or clarify any bid that fails to define the outcome, data access, evaluation method, accountable roles, recurring costs or an exit path.
Use one weighted 100-point matrix for every finalist, apply identical evidence standards, and normalize total cost before selecting a provider. The UK Sourcing Playbook recommends should-cost analysis to counter low-cost bid bias. World Bank rated-criteria guidance treats methodology, risk, key personnel and capacity as legitimate non-price attributes. AI procurement must extend those disciplines to testing, model behavior, governance and production operations.
The MAKINAI Proposal Evidence Score-100
This scorecard separates polished sales narratives from delivery readiness. Score each area from zero to five, then apply the weight. Zero means missing; one means a generic assertion; three means a plausible approach with partial artifacts; five means specific, verifiable evidence aligned to your environment. Lock the weights before opening proposals so an attractive presentation cannot quietly redefine the decision.
- Outcome and baseline — 15 points: decision to improve, current metric, target, affected users and use-case boundary.
- Architecture, data and integration — 15 points: sources, quality, permissions, APIs, identity, environments and dependencies.
- Evaluation and safety — 15 points: test set, acceptance thresholds, human review, misuse, privacy and monitoring.
- Delivery method — 15 points: phases, deliverables, cadence, owners, risks and go/no-go criteria.
- Team and applicable experience — 10 points: named people, availability, roles and verifiable examples of comparable work.
- Operations and governance — 10 points: SLOs, incidents, model changes, records, auditability and accountability.
- Total economics — 10 points: professional services, models, cloud, data, licenses, support and volume scenarios.
- Transfer and exit — 10 points: intellectual property, repositories, documentation, training, portability and transition.
Five gates to apply before scoring
A high average should never compensate for a structural risk. Disqualify or return for clarification any proposal that hides pricing assumptions, cannot identify who can access company data, refuses objective acceptance criteria, depends on an uncommitted key individual, or prevents the buyer from recovering data, configurations, prompts, code and documentation at exit. For higher-impact use cases, add the legal and regulatory requirements that apply to the deployment and market.
Do not accept a single accuracy claim as the evaluation plan. Require task-level measures: answer quality, evidence retrieval, completion rate, critical errors, latency, cost per transaction, human intervention and impact on the business metric. The NIST AI RMF organizes work around Govern, Map, Measure and Manage. A mature proposal turns those functions into named activities, owners and reviewable artifacts.
Normalize price without rewarding missing scope
Build a simple should-cost model before comparing bids. Estimate hours by role, infrastructure, model usage, storage, tools, support and operations under three volume scenarios. Require every provider to complete the same structure. A bid that is 20 percent cheaper may cost more if it omits evaluation, observability, data remediation or transition. Compare 12-to-24-month total cost, not only the discovery or build phase.
Separate fixed, variable and conditional charges. Request unit rates and adjustment triggers. For model consumption, simulate a normal month, a peak and a growth case. For managed services, document what the SLA includes and which events become billable requests. Any difference that cannot be normalized belongs in the risk register rather than disappearing inside an average score.
Use the finalist session to test the proposed team
Invite the top two or three bidders to the same 90-minute evidence session. Send a small scenario, synthetic data and four disruptions in advance: an unavailable source, a revoked permission, conflicting model output and a volume spike. Ask the proposed delivery team to explain decisions, trade-offs, tests and business communication. Score who actually attends; strong sellers are not substitutes for the architects, data specialists, product leads and change practitioners who will do the work.
Use identical questions, time limits and evaluators. Each evaluator records a score before group discussion. The committee then reviews only material differences and documents the final rationale. This limits hierarchy effects, presentation bias and post-hoc changes to the criteria.
Make the trade-offs explicit
- Specialist versus integrator: deep use-case expertise may come with less capacity for enterprise-wide change.
- Speed versus control: packaged components accelerate delivery but can constrain portability or customization.
- Single provider versus modular architecture: one accountable party simplifies governance; modules reduce dependency but increase coordination.
- Fixed price versus iterative delivery: fixed price protects stable scope; uncertain AI work may benefit from staged funding and evidence gates.
- Capability transfer versus continuous operation: internalization builds autonomy; managed service can reduce time to operate if the exit path is tested.
A four-step decision protocol
First, confirm mandatory gates, conflicts and material exceptions. Second, complete individual scoring and an evidence-based calibration session. Third, normalize total cost and conduct references only for genuinely comparable delivery. Fourth, convert remaining uncertainty into contract conditions, acceptance milestones or a paid pilot. The winner is not simply the highest abstract score; it is the option with the best combination of value, evidence and acceptable residual risk.
If you are still designing the procurement, start with MAKINAI's RFP guide at https://makinai.co/insights/en/how-to-write-rfp-ai-services. To validate finalists before a larger commitment, use https://makinai.co/insights/en/how-to-run-paid-ai-pilot-before-hiring-partner. When turning the decision into obligations, review https://makinai.co/insights/en/what-to-include-ai-services-contract-sow.
When to involve MAKINAI
MAKINAI can help design the evaluation matrix, review competing proposals and run a technical-commercial validation without replacing the buyer's accountability. The purpose is to make assumptions, risks and evidence comparable—and select an engagement that can be measured, operated and transferred.