Is There Any AI Better Than ChatGPT?

When developers, founders, and operators start looking at generative AI for their products, the first name that comes to mind is often ChatGPT. It's a solid baseline, but the landscape has expanded quickly. Multiple models now compete on capability, cost‑effectiveness, privacy, and integration flexibility. In this post we'll explore the practical dimensions that matter when deciding whether another AI might serve your business better than ChatGPT, and how a multi‑model platform like Better AI can simplify the evaluation and deployment process.

1. What "Better" Means in a Business Context

"Better" is rarely a universal superlative. It depends on the specific problems you're solving. Below are the common criteria teams use to compare language models:

| Criterion | Why It Matters | Typical Trade‑offs | |-----------|----------------|--------------------| | Domain Knowledge | Some industries (legal, medical, finance) need terminology that generic models lack. | Specialized models may require more data engineering. | | Latency & Throughput | Real‑time chat or API calls need sub‑second responses. | Larger models can be slower; smaller, distilled versions run faster. | | Data Privacy | Sensitive user data must stay on‑prem or within a trusted cloud region. | Private deployments limit easy access to the latest updates. | | Customization | Fine‑tuning allows the model to adopt your brand voice or business rules. | Custom training incurs engineering effort and ongoing maintenance. | | Cost Effectiveness | Pay‑per‑token pricing can add up for high‑volume workloads. | Open‑source models can be cheaper but require infrastructure overhead. | | Ecosystem & Tooling | Seamless SDKs, libraries, and monitoring simplify integration. | A rich ecosystem may lock you into a particular stack. |

By mapping your project requirements to these criteria, you can determine whether ChatGPT is sufficient or if another AI system could provide a better fit.

2. Notable Alternatives to ChatGPT

Below is a concise overview of the most relevant families of models that businesses consider today. Each entry includes the core strength and a scenario where it might outperform ChatGPT.

2.1 Open‑Source Large Language Models (LLMs)

| Model | Core Strength | When It Might Beat ChatGPT | |-------|---------------|----------------------------| | LLaMA‑2 (Meta) | Strong academic benchmarking, permissive licensing. | When you need a model you can run on‑premises for strict data governance. | | Mistral | Small footprint with competitive reasoning ability. | When you prioritize low latency on modest hardware. | | Gemma (Google) | Designed for instruction following with less hallucination. | When consistent factuality is critical for customer support bots. |

Practical tip: Use a container‑based runtime (e.g., Docker) to spin up these models locally, then expose them via a lightweight API gateway. This gives you full control over versioning and security.

2.2 Commercial "Specialized" APIs

| Provider | Core Strength | When It Might Beat ChatGPT | |----------|---------------|----------------------------| | Claude (Anthropic) | Emphasis on safety and reduced toxic output. | When your application must adhere to rigorous content moderation standards. | | Gemini (Google) | Strong multimodal capability (text + vision). | When you need to process images alongside text, such as OCR‑enhanced chat. | | Cohere Command | Optimized for long‑form summarization and classification. | When you regularly generate detailed reports from large documents. |

These services often expose both chat‑style and completions‑style endpoints, allowing you to pick the interface that aligns with your workflow.

2.3 Enterprise‑Grade Platforms with Agent Orchestration

| Platform | Core Strength | When It Might Beat ChatGPT | |----------|---------------|----------------------------| | Microsoft Azure OpenAI Service | Tight integration with Azure identity, compliance, and data pipelines. | When you already rely heavily on Azure services and need unified access control. | | AWS Bedrock | Managed hosting for multiple foundation models, including Titan and Jurassic‑2. | When you prefer a serverless billing model that abstracts infrastructure. | | Better AI (multi‑model SaaS) | Unified chat, API, and AI agents across several models, with built‑in routing and monitoring. | When you want to experiment with several models without managing separate accounts or deployments. |

The key advantage of these platforms is that they let you switch underlying models with minimal code changes, helping you benchmark performance directly against your own KPIs.

3. How to Run a Quick Comparative Test

A disciplined test can reveal which model aligns best with your requirements. Follow these steps:

1. Define Representative Tasks

Choose 3–5 real user scenarios (e.g., ticket triage, product description generation, code review assistance). Write clear prompts and expected output criteria.

1. Select Evaluation Metrics

• Relevance: Does the response answer the prompt?
• Factuality: Are the statements correct?
• Tone Consistency: Does it match your brand voice?
• Latency: Measure round‑trip time for each API call.
• Cost per Token: Track usage statistics from each provider.

1. Set Up a Minimal Test Harness

   import time, json, httpx

   models = {
       "chatgpt": "https://api.openai.com/v1/chat/completions",
       "claude": "https://api.anthropic.com/v1/complete",
       "llama": "http://localhost:8000/v1/completions"
   }

   def call(model_url, payload, headers):
       start = time.time()
       resp = httpx.post(model_url, json=payload, headers=headers, timeout=30.0)
       elapsed = time.time() - start
       return resp.json(), elapsed

Keep the harness simple; focus on repeatable measurements rather than production‑grade error handling.

1. Run Multiple Iterations

Execute each prompt at least five times per model to smooth out variability, then average the results.

1. Analyze and Decide

Summarize findings in a table, weight each metric according to business impact, and calculate a simple score.

Takeaway: Even a modest test conducted over a day can surface decisive differences, such as a 30 % reduction in latency for a smaller model or a noticeable improvement in factuality for a safety‑focused provider.

4. When to Stay with ChatGPT

ChatGPT remains a strong choice in several situations:

• Broad General‑Purpose Use: If your workload spans many topics without deep domain specificity, the model's wide knowledge base is advantageous.
• Rapid Prototyping: Its well‑documented SDKs and extensive community examples accelerate early development.
• Integrated Tooling: OpenAI's ecosystem (e.g., function calling, fine‑tuning pipelines) makes it easy to build structured outputs and custom assistants.
• Scalable Cloud Offering: The managed service handles scaling, monitoring, and reliability out of the box, helping teams avoid operational overhead.

If these factors align with your priorities, you may not need to switch immediately. However, keep an eye on emerging models; the field evolves quickly.

5. Leveraging a Multi‑Model Platform

Managing multiple APIs, authentication schemes, and monitoring dashboards can become a distraction. A platform that abstracts the underlying models while exposing a unified interface can bring several practical benefits:

• Single SDK: Write one set of client code; the platform routes requests to the selected model.
• Dynamic Model Switching: Change the target model via configuration or feature flag without redeploying.
• Built‑In Observability: Centralized logs and latency dashboards help you spot regressions early.
• Policy‑Driven Guardrails: Apply content moderation or rate limits uniformly across models.

Better AI provides exactly this kind of multi‑model experience. By plugging into its chat, API, and agent layers, you can experiment with LLaMA‑2, Claude, and other options side‑by‑side, then settle on the model that meets your quality, speed, and cost targets.

6. Practical Checklist Before Deciding

• [ ] Identify core use cases and list the most important evaluation criteria.
• [ ] Run a lightweight benchmark using the method above.
• [ ] Consider data residency requirements; decide if on‑premise or private cloud deployment is mandatory.
• [ ] Estimate total cost of ownership, including engineering effort for custom deployments.
• [ ] Map integration points (CRM, ticketing, CI/CD) to see which platform offers the smoothest connectors.
• [ ] Plan for future scaling—ensure the chosen service can handle anticipated traffic growth without major re‑architecting.

By ticking these items off, you move from "ChatGPT is popular" to "We have evidence that this model meets our needs".

Step‑by‑Step Walkthrough: Taking a Multi‑Model Pilot from Zero to Production

The comparative test in Section 3 tells you which model looks best on paper. Getting from there to a production deployment is a different exercise. Here's a walkthrough you can adapt to your stack, structured in five phases.

Phase 1: Freeze the Prompt Contract

Before touching infrastructure, formalize how your application talks to the model. Create a versioned prompt template file rather than embedding prompts in code:

# prompts/ticket_triage.v1.yaml
name: ticket_triage
version: 1
system: |
  You are a support triage assistant for {company_name}.
  Classify the ticket into exactly one category and output strict JSON.
user: |
  Ticket subject: {subject}
  Ticket body: {body}
  Categories: billing, bug, feature_request, other
output_format: json

Three reasons this matters:

1. Portability. When you swap models, you change one file, not a dozen code paths.
2. Auditability. You can diff prompt versions the same way you diff code.
3. A/B testing. Version identifiers let you route a percentage of traffic to a new prompt safely.

Phase 2: Normalize Inputs and Outputs Across Models

Models differ in how they handle system messages, JSON mode, and stop sequences. Abstract these differences behind a thin adapter layer:

class ModelAdapter:
    def format_request(self, prompt: dict, variables: dict) -> dict: ...
    def parse_response(self, raw: dict) -> dict: ...

At minimum, normalize:

• System vs. user role handling — some models treat system prompts as strong instructions; others blend them into the conversation.
• Structured output enforcement — where JSON mode or function calling exists, use it; where it doesn't, validate the output with a schema validator (e.g., Pydantic) and retry once on failure.
• Token accounting — log prompt tokens and completion tokens separately per request, since pricing models differ between them.

If you use a unified platform like Better AI, much of this normalization is handled at the platform level, which is precisely why teams adopt one — but writing the adapter explicitly during your pilot phase teaches you where the real incompatibilities live.

Phase 3: Route, Don't Commit

Instead of hardcoding a single model, introduce routing rules from day one:

def select_model(task_type: str, payload_size: int) -> str:
    if task_type == "classification":
        return "small_fast_model"       # cheap, low latency
    if payload_size > 50_000:
        return "long_context_model"     # handles large documents
    return "default_model"

Common routing strategies worth piloting:

| Strategy | How It Works | Best For | |----------|--------------|----------| | Task-based routing | Different task types go to different models. | Mixed workloads (triage vs. long-form writing). | | Cascade routing | Try a cheaper model first; escalate to a stronger one if validation fails. | High-volume classification, extraction. | | Shadow routing | Send a copy of production traffic to a candidate model without serving its output. | Risk-free model comparison on real data. | | Time-based fallback | Fall back to a secondary provider on rate limits or outages. | Anything customer-facing. |

Shadow routing deserves special attention: it lets you evaluate a candidate model against live traffic before users ever see its output. Log both responses, score them offline, and you'll have a far more honest comparison than any benchmark suite can offer.

Phase 4: Instrument Before You Scale

Add these four signals to your observability stack during the pilot, not after launch:

1. Latency percentiles (p50 and p95), not just averages — a model with a great average but terrible tail latency will frustrate real users.
2. Output validity rate — the fraction of responses that pass your schema/format validation.
3. User signals — thumbs up/down, regeneration clicks, escalation to a human agent.
4. Cost per successful outcome — cost per request is misleading; some models are cheaper per token but require more retries.

Phase 5: Ship Behind a Flag, Then Promote

Release the new model to a small slice of traffic behind a feature flag. Watch the four signals above for at least one full business cycle (a week captures weekend traffic patterns that a two-day test will miss). If the candidate matches or beats the incumbent on your weighted score, promote it. If not, you've lost almost nothing — which is the entire point of keeping the abstraction layer in place.

Real‑World Scenarios: How Different Teams Decide

Abstract criteria become clearer with concrete situations. These composite scenarios reflect patterns we see repeatedly among teams evaluating alternatives.

Scenario A: A B2B SaaS Support Bot with Compliance Constraints

Situation: A 20-person SaaS company wants a support chatbot. Customers paste screenshots, error logs, and occasionally account details into tickets. Their enterprise prospects ask pointed questions about data handling during security reviews.

Reasoning path:

• Screenshots in tickets push them toward a multimodal-capable model, but only for the classification step — the text-only responder can handle the rest.
• Enterprise procurement pressure means they need data processing agreements and clear retention terms regardless of which provider they choose.
• Ticket volume is moderate, so cost per token matters less than consistency and auditability.

Outcome pattern: Teams in this position often land on a two-model setup: a multimodal model for intake/classification and a safety-focused commercial model for customer-facing responses, both behind a single platform so the security review only covers one data flow diagram.

Scenario B: An E-Commerce Content Operations Team

Situation: A mid-market retailer generates product descriptions, category pages, and marketing emails. Volume is high (thousands of SKUs), latency is irrelevant (batch jobs), but brand voice consistency is paramount, and margins are thin.

Reasoning path:

• Latency criteria drop out entirely; cost and quality consistency dominate.
• High volume justifies investing in a strong style guide embedded in prompts, plus automated checks (reading level, banned words, required fields) that run on every output.
• Because the task is template-driven, a smaller model with a well-engineered prompt often matches a flagship model's quality at a fraction of the cost.

Outcome pattern: Cascade routing shines here — run the cheap model on everything, run automated quality checks, and escalate only the failures (typically a small minority) to a stronger model.

Scenario C: A Developer Tools Startup Building an AI Feature

Situation: A startup is adding a "explain this error" feature to their product. Their users are developers; the feature must feel instant, and the company wants to avoid building model infrastructure at all.

Reasoning path:

• Sub-second perceived latency is the make-or-break criterion; they plan to stream tokens and show partial output immediately.
• They will iterate on this feature for months, so provider flexibility matters more than squeezing the last percent of quality out of any one model today.
• Handling rate limits gracefully matters because traffic will be spiky at launch.

Outcome pattern: A unified multi-model API with automatic fallback — accessible via a platform like Better AI — lets the team ship fast while keeping the door open to swap models as the feature matures and their prompt engineering improves.

Scenario D: A Financial Services Firm Processing Long Documents

Situation: An operations team summarizes 100+ page regulatory filings. Retrieval-augmented generation (RAG) is on their roadmap, but they haven't built it yet.

Reasoning path:

• Long-context handling is the near-term differentiator; some models accept very large inputs directly, which delays the need to build a full RAG pipeline.
• However, long-context windows are not a free lunch — models can lose track of details buried in the middle of very long inputs, so they must test on their documents, not generic benchmarks.
• Factuality requirements are strict enough that every summary needs citation-style grounding to the source text.

Outcome pattern: They benchmark candidate models on their actual filings with a "needle in a haystack" style test adapted to their domain — asking about specific clauses positioned at various depths within the document — before committing.

Common Mistakes When Evaluating AI Alternatives (and How to Avoid Them)

These failure patterns show up across teams of every size. Each is avoidable with a little forethought.

1. Benchmarking with cherry-picked prompts. If you write the test prompts the same day you run the test, you'll unconsciously write prompts that suit your favorite model. Instead, collect real prompts (or prompts close to real ones) from your product, support tickets, or logs before the evaluation begins.

2. Confusing benchmark leaderboards with business fit. Public benchmarks measure capabilities you may never use. A model that tops a reasoning leaderboard can still lose to a smaller model on your narrow classification task. Weight your own eval results far above any leaderboard.

3. Testing only the happy path. Real usage includes typos, mixed languages, hostile inputs, and ambiguous requests. Reserve 20–30% of your test set for adversarial and edge-case inputs — you'll often find that the model ranking flips on them.

4. Ignoring the tail of the latency distribution. Averages hide the slowest 5% of requests, and those are the ones users remember. Track p95 (and p99 if you're customer-facing at scale).

5. Forgetting to cost retries. A cheaper model that fails JSON validation 15% of the time can cost more per successful output than a pricier model that succeeds first try. Always divide cost by successful outcomes.

6. Evaluating once and never again. Model providers update their offerings regularly, and a model that was right for you six months ago may no longer be. Build a small, repeatable eval suite and re-run it quarterly.

7. Testing with a prompt tuned for a different model. Prompts are not perfectly portable. A verbose chain-of-thought prompt tuned for one model may degrade another. Give each candidate model a fair prompt-tuning pass before scoring it — otherwise you're measuring prompt compatibility, not model capability.

8. Switching providers without an exit plan. Swapping one vendor for another after a year of accumulated prompt tweaks, fine-tuning assumptions, and integration code can take longer than the original build. Architect for portability from day one (see the lock-in section below).

9. Over-rotating on a single impressive demo. One brilliant output from a model tells you it can perform, not that it consistently performs. Insist on statistically meaningful sample sizes before drawing conclusions.

Edge Cases and Deeper Guidance

Most evaluations stumble not on the main path but on the edges. Here is targeted guidance for the situations that most often cause trouble.

| Edge Case | What Goes Wrong | Practical Mitigation | |-----------|-----------------|----------------------| | Very long inputs | Models with large context windows may still underweight information in the middle of the input. | Test with your real documents; consider chunking and summarizing sections before final synthesis. | | Multilingual traffic | Quality varies significantly by language, even within one model. | Evaluate each target language separately; route by detected language if needed. | | Strict JSON/structured output | Models without native structured-output modes occasionally break format. | Use schema validation with one retry; prefer models with native JSON/function-calling modes for these tasks. | | Streaming responses | Token throughput differences change perceived speed even when total time is similar. | Measure time-to-first-token separately from total completion time. | | Rate limits at peak | Bursty traffic can exhaust a single provider's quota. | Implement multi-provider fallback with backoff; queue non-urgent work. | | Model deprecation | Providers retire models, forcing migration under time pressure. | Maintain a validated fallback model and re-run your eval suite against it quarterly. | | Hallucination in RAG | A grounded system can still invent details not present in retrieved context. | Require inline citations to retrieved chunks and reject answers lacking them. | | Prompt injection | Untrusted text in user input can override your instructions. | Treat all user content as data, not instructions; sanitize and scope system prompts tightly. |

A few of these deserve elaboration.

On multilingual workloads: don't assume a model's English quality transfers. Build a small test set per language you actually serve — even five to ten real examples per language — and score them independently. It's common for a model to excel in one language and lag in another, which is invisible in aggregate testing.

On time-to-first-token: if you're building a chat experience, users judge speed by how quickly something appears. Two models with identical total generation time can feel dramatically different when one streams its first token in 300 ms and the other takes 1.5 seconds. Include this metric in any latency comparison for interactive products.

On prompt injection: any application that feeds user text — or content scraped from the web — into a model alongside instructions is exposed. The core defense is architectural: keep untrusted content in clearly delimited user-message slots, never concatenate it into system prompts, and design the system so that even a fully compromised model response can only do limited harm (e.g., an agent with read-only tools rather than write access).

Cost Modeling Without Guesswork

Sticker-price token costs are the beginning of the cost conversation, not the end. Build a simple spreadsheet with these components before you commit:

| Cost Component | What to Include | Commonly Missed? | |----------------|-----------------|------------------| | Inference (API) | Prompt + completion tokens × volume × per-token price; volume typically grows faster than you model. | Yes — teams model launch volume, not year-one volume. | | Retries and validation | Failed or rejected outputs that must be regenerated. | Yes — can materially change effective price. | | Engineering time | Integration, prompt engineering, eval harness, monitoring. | Yes — often the largest line item for self-hosted options. | | Infrastructure (self-hosted) | GPU instances, storage, networking, idle capacity. | Yes — GPUs are rarely fully utilized. | | Ops and on-call | Model updates, dependency upgrades, incident response for self-hosted stacks. | Yes — recurring and easy to underestimate. | | Egress and middleware | Platform fees, data transfer between services, caching layers. | Sometimes. |

Two structural notes:

• Cost per task ≠ cost per token. A model that needs a longer prompt to perform well can cost more per task than a model with a higher per-token price and a shorter prompt. Normalize your comparison to cost per completed, validated task.
• Volume changes the ranking. Strategies that make sense at hundreds of requests per day (a single commercial API) may not at millions (cascades, caching, self-hosting for the predictable portion of traffic). Revisit the model annually as your usage grows.

Avoiding Vendor Lock-In: An Exit-Ready Architecture

Whatever you choose today, you should be able to change your mind later without a rewrite. Use this checklist to assess how exit-ready your architecture is:

| Lock-In Vector | Exit-Ready Practice | |----------------|---------------------| | API shape | Use an adapter layer or a multi-model platform's unified API rather than calling provider SDKs directly throughout your codebase. | | Prompts | Store prompts as versioned configuration, not inline strings; keep them model-agnostic where possible. | | Fine-tuned assets | Document the training data and recipe so a fine-tune can be reproduced on a different base model if required. | | Model IDs in code | Reference models through logical names (summarizer, classifier) mapped to concrete models in config. | | Data formats | Persist request/response logs in a neutral schema so historical evals remain comparable across providers. | | Contractual | Understand data retention and export terms before you send real user data to any provider. |

The cheapest insurance policy here is a quarterly "swap drill": point a staging environment at your fallback model, run your eval suite, and confirm nothing breaks. Teams that do this can migrate in days; teams that don't often measure the migration in quarters.

Building a Lightweight Ongoing Eval Loop

A one-time evaluation goes stale. The good news is that a sustainable eval loop can be small. Here's a minimal recipe:

1. Golden set: Keep 30–100 real examples with accepted outputs (or graded criteria). Refresh it as your product evolves — add examples whenever support tickets or user feedback reveal a new failure pattern.
2. Automated checks: Format validation, banned-content checks, and schema compliance run on every deploy. These catch mechanical regressions instantly.
3. LLM-as-judge scoring (with care): For subjective qualities like tone, a strong model can grade candidate outputs against a rubric. Validate the judge against your own human grading on a sample first — judges have biases too.
4. Human review budget: Even 20 manually reviewed outputs per month keeps you calibrated to reality in a way no automation fully replaces.
5. Change triggers: Re-run the full suite whenever you change a prompt, swap a model, or learn that a provider has shipped a model update.

A platform with centralized logging and per-model dashboards — as described in Section 5 — makes this loop dramatically easier, because every request and response is already captured in a comparable format without extra instrumentation work on your side.

Frequently Asked Questions

Is open-source actually viable for production, or just for experiments?

It depends on your constraints more than on the models themselves. Open-source models are genuinely production-viable — teams run them for classification, extraction, and even chat daily. The real questions are operational: do you have (or want to build) the skills to run, monitor, and update model infrastructure? If your team is small and your differentiation doesn't come from serving models yourself, a managed multi-model platform is usually the pragmatic path. If strict data residency is non-negotiable, self-hosting an open-source model — or using a platform that offers private deployment options — becomes the primary criterion, and capability comparisons move to second place.

How often should I re-evaluate my model choice?

Quarterly is a sensible cadence for most teams, plus event-driven re-evaluations when you hear about a relevant model release, when your provider announces deprecations, or when your own quality metrics drift. The eval loop described above is designed to make a quarterly re-run cost a few hours rather than a few weeks.

Can I use multiple models in the same product?

Yes, and it's increasingly the norm. The most common pattern is routing by task: a fast, inexpensive model for high-volume simple tasks (classification, routing, short rewrites) and a stronger model for complex ones (analysis, long-form generation, agentic reasoning). The main engineering cost is maintaining the abstraction that keeps this manageable — which is exactly the problem multi-model platforms exist to solve.

Do I need to fine-tune a model to compete with ChatGPT?

Usually not as a first step. Prompt engineering, retrieval augmentation, and few-shot examples resolve the majority of "the model doesn't know our domain" situations. Fine-tuning earns its keep when you need consistent style at high volume, when you've genuinely exhausted prompt-based approaches, or when a smaller fine-tuned model can replace a larger prompted one for cost reasons. Treat fine-tuning as an optimization, not a prerequisite.

How do I handle a model provider retiring a model I depend on?

This is a when, not an if. Your defenses are architectural: logical model names in config, a validated fallback model, and a golden-set eval suite you can run against any candidate replacement in an afternoon. When a deprecation notice arrives, run your suite against shortlisted replacements, review the diffs on a sample of real outputs, update the config mapping, and ship behind a flag. Teams with this in place treat deprecations as routine; teams without them treat them as fires.

What's the single biggest predictor of success when switching from ChatGPT to an alternative?

Rigor of the evaluation. Teams that test on real prompts, measure cost per successful outcome, include adversarial inputs, and pilot behind a feature flag rarely regret their switch. Teams that decide from demos and leaderboards frequently roll back. The model choice itself matters, but the process quality around the choice matters more.

Does using a multi-model platform add meaningful latency or cost?

A thin routing layer adds negligible overhead compared to model inference times — the routing decision itself is microseconds against response times measured in hundreds of milliseconds or seconds. The cost question is better framed as a trade: a modest platform fee versus the engineering hours you'd spend building and maintaining adapters, fallback logic, logging, and eval infrastructure yourself. For most teams past the prototype stage, the platform route is the cheaper option in total cost of ownership — but do your own arithmetic with the cost model from the section above.

Final Thoughts

There isn't a single answer to "Is there any AI better than ChatGPT?" The right model depends on the blend of domain expertise, latency, privacy, and cost considerations that matter to your business. Conducting a focused evaluation, leveraging a multi-model platform for easy experimentation, and continuously monitoring real-world performance will keep you from over-committing to any one provider.

The teams that win with AI aren't the ones that picked the "best" model once — they're the ones that built the muscle to evaluate, route, and switch as the landscape evolves. Keep your architecture portable, keep your evals current, and the question stops being stressful and starts being a routine optimization.

If you're looking for a streamlined way to test several models side-by-side and integrate them into chat, API, or autonomous agents, explore the Better AI platform at https://betteraisoftware.com.