Blog 106: Assessment parameters of players in the ‘AI app development & infrastructure’ segment

A business firm recently requested that we assess ‘AI app development and infrastructure’* products to help them select the suitable ones for their ongoing AI implementations.

* Refers to the specialised combination of hardware, software, and practices required to design, build, train, deploy, and manage artificial intelligence (AI) applications at scale.

There are broadly two categories of products / frameworks within the ‘AI app development and infrastructure’* segment. Additionally, a third category has emerged in recent times.

1. AI products that are low code workflow automation tools (e.g., n8n).

• Connect various apps and services, automate tasks, and build workflows through a visual interface.

• Integrate with AI models as part of a larger automation flow.

2. AI products or frameworks for developing applications powered by large language models (e.g., LangChain).

• Provides tools for building LLM-based applications. Can integrate with various sources and external tools.

• Includes prompt management, memory etc.

There is now a third category. The type that can upgrade your non-AI app to an AI-enhanced app in just a few hours. i.e.,

3. AI products that can inject micro intelligence into your existing non-AI app (e.g., Rightbrain.ai).

• These products enable you to build AI capabilities (using prompts & LLMs).

• Capabilities such as competitor analysis (i.e., research and summarise a company’s top competitors) etc.

• After building out these capabilities you can inject them into your existing app/agent (leveraging API, MCP, A2A).

To analyse 20+ leading global players within this segment we arrived at 50 assessment parameters across 8 groups.

1. Product and market

2. Core features and capabilities

3. Integrations and extensibility

4. Observability and operations

5. Data, privacy and security

6. Performance and quality

7. User experience and productivity

8. Commercial business

Group 1: Product and market

1.Primary use case / positioning: What problem(s) the product explicitly targets (RAG, agents, tool orchestration, internal automation, chatbots).

2.Target customer profile: Small and medium businesses, startups, enterprise, regulated industries, research labs, etc.

3.Maturity & traction: Years in market, customers, public case studies, growth signals.

4.Differentiated value proposition: What is unique (e.g., tasks, model comparison, replay).

5.Product roadmap transparency: Frequency/clarity of roadmap, public changelog.

Group 2: Core features and capabilities

6.Task / workflow abstraction: Are prompts/workflows first-class objects you can version and deploy?

7.Multi-model orchestration: Ability to plug and orchestrate multiple LLMs in a single pipeline.

8.Model comparison / A/B testing: Built-in comparison, evaluation metrics, side-by-side runs.

9.Tool/agent integration: Native connectors to APIs, databases, web, actions, tools.

10.RAG & retrieval support: Built-in vector DB support, retrievers, embedding pipelines.

11.Template / starter library: Prebuilt task templates, marketplace etc.

12.Fine-tuning / custom models: Support for fine-tuning or custom model hosting.

13.Prompt management: Prompt versioning, templating, parameterization, testing etc.

14.Memory / state management: Memory primitives for multi-turn agents (handle back-and-forth conversations) or long context retention.

15.Batch & streaming support: Synchronous/asynchronous execution, streaming outputs.

Group 3: Integrations and extensibility

16.API & SDK footprint: Quality and breadth of SDKs (Python, JS, REST) and APIs.

17.Third-party model support: Which model providers are supported (OpenAI, Anthropic etc).

18.Data connectors: Built-in ingestion connectors (S3, Google Drive, Salesforce, DBs, Slack).

19.Plugin / extension ecosystem: Marketplace for community plugins or integrations.

20.Custom code / web hooks: Ability to embed custom code, webhooks, or lambda functions.

Group 4: Observability and operations

21.Logging & request traces: Granularity of logs, ability to replay calls and trace flows.

22.Cost/usage observability: Per-task cost, credit accounting, cost forecasting.

23.Latency & performance metrics: Per-task latency, percentile metrics and SLO (service level objectives) tracking.

24.Error handling & retries: Built-in policies for failures, retries, fallbacks.

25.Versioning & release management: Version control for tasks, safe rollout / canary features.

26.Monitoring & alerts: Alerts for error spikes, latency, cost thresholds, SLO violations.

27.CI/CD / Git integrations: GitOps support, PR review, automated deployment hooks.

Group 5: Data, privacy and security

28.Data residency & isolation: Region controls, data localization options.

29.Encryption & key management: At-rest/in-transit encryption, BYOK / customer key management service (KMS).

30.Access control & RBAC: Granular role-based access, least privilege controls.

31.Private model hosting / on-prem options: Ability to use private or on-prem models.

32.Data retention & deletion policies: Retention defaults, deletion on request, audit logs.

33.Compliance & certifications: SOC2, ISO27k, HIPAA, GDPR readiness and documentation.

34.Auditability & reproducibility: Replay able runs, immutable logs, audit trails for decisions.

Group 6: Performance and quality

35.Output quality controls: Built-in evaluation metrics, hallucination detection, guardrails.

36.Throughput & scaling: Concurrency limits, auto-scaling behaviour, throttling policies.

37.Model selection & routing logic: Smart routing between cheaper/expensive models per request.

38.Cache & cold-start optimisations: Response caching, warmup strategies, cost reduction features.

39.Evaluation tooling: Ground truth testing, scoring, human-in-the-loop review flows.

Group 7: User experience and productivity

40.UI/UX for builders: Quality of interface for non-engineers and engineers.

41.Developer ergonomics: CLI tools, SDK quality, playgrounds, local dev workflows.

42.Onboarding & documentation: Step-by-step guides, tutorials, sample apps.

43.Team collaboration features: Shared workspaces, comments, assignment, work history.

44.Delight features: Smart templates, visual diffing (comparing two versions), intuitive error messages.

Group 8: Commercial business

45.Pricing model transparency: Clear pricing, breakdown of credits/calls, overage policies.

46.Total cost of ownership (TCO) levers: Predictability of costs, ability to optimize spend.

47.Support & SLAs: Support options, response times, enterprise SLAs, onboarding assistance.

48.Partner & integration network: ISV (independent software vendors) partners, consultancies, marketplace reach.

49.Contracts & negotiation flexibility: Enterprise contracting, custom terms, volume discounts.

50.Community & ecosystem health: Active forums, GitHub activity, third-party extensions and resources.

As AI continues to advance quickly, numerous aspects could shift, meaning this blog might eventually need a complete update.

We are attaching a template for your use. Use them as a guide and adapt as needed.

Jump to blog 100 to refer to the overall product management mind map.

I wish you the best for your journey. 😊

Idea2product2business_Assessment parameters_template.pdf

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