What 12 Projects Taught Us About Shipping AI Products

CODERCOPS built 12 projects in 2024: eight for clients and four internal builds. They cover healthcare (The Venting Spot, Excellence Healthcare), e-commerce (Colleatz), Web3 (Lore), career tech (AI Interview), data analytics (QueryLytic), security (StickGuard), lifestyle and community (Plantree), non-profit (Parivartan Samiti), food service (Sarmistha’s Cloud Kitchen), hospitality (Luxury Lodgings) and creative work (Glassfolio).

Four of them are AI products in the plain sense: The Venting Spot, QueryLytic, AI Interview and Lore all put an OpenAI model at the center of a feature. The rest are not AI-heavy at all, and that contrast is useful. A lot of what decides whether an AI product works has nothing to do with the model.

I have organized this by theme rather than by project, because the most useful ideas are the ones that apply across different contexts.

Twelve projects, one year, a lot of different industries. Here is what carries over.

Lesson 1: Clients think they want AI. What they want is automation.

Clients ask for AI because it is the word everyone uses, but the underlying need is usually automation: a manual step turned into something that happens on its own. Decomposing the request into “what should stop being manual?” is the fastest way to scope an AI feature correctly, and sometimes it shows the feature does not need AI at all.

The Venting Spot is a good example of AI doing real work. Its matching uses OpenAI GPT models to pair a user’s selected mood (stressed, lonely, heartbroken, overworked) with an appropriate listener. The model is real. But what the user values is not the model. It is not having to scroll through 500+ listener profiles while they are upset. The AI enables the automation, and the automation is the value.

Sarmistha’s Cloud Kitchen shows the same instinct without any AI. The business needed online ordering but did not need the complexity of a full e-commerce system, so the site sends orders to WhatsApp with the order details pre-filled. The manual step (taking orders over the phone) got smaller, and nobody had to build a checkout.

Getting to the right scope depends on asking the right questions during client discovery rather than accepting the first framing. Part of being an AI-first agency is knowing when AI is the wrong answer, which is a question to put into the discovery call, before scoping starts.

What clients ask for	What they usually need	Right approach
”A chatbot that answers everything”	A chatbot that handles the common questions and hands off the rest gracefully	LLM plus a human handoff workflow
”AI-powered recommendations”	Relevant suggestions when users are undecided	Rules for small catalogs, AI for large ones
”Fully automated content generation”	AI-drafted content with human review	Generation plus a review UI
”Real-time AI analysis”	Results that are fresh enough, which is usually not real time	Background jobs plus a cache layer
”Custom AI model”	Prompt engineering on a foundation model	A hosted model API plus careful prompts

Request Decomposition Framework

"We want AI for X"
        |
        v
  What manual process does X replace?
        |
        v
  Can a rule-based system handle most cases?
        |
  +-----+-----+
  |           |
  Yes         No
  |           |
  v           v
  Build rules   Use AI
  + AI for     for core
  edge cases   logic
  |           |
  v           v
  Cheaper,     More capable,
  faster,      higher cost,
  predictable  needs fallbacks

Lesson 2: The demo always works. Production always breaks.

Demos use clean, controlled inputs. Real users do not. That gap is the most predictable failure in AI features, and it is easy to plan for.

Take a natural-language interface like QueryLytic, where a user types a question in English and gets a database query back. Testing it with well-formed questions (“Show me all orders from last month”) tells you almost nothing. The inputs worth testing look like this:

• A single keyword with no question (“orders”)
• A long, ambiguous request (“whats the thing with the most sales last month not including returns”)
• Actual SQL typed into the natural-language box
• A question with no time range or metric (“How much money did we make”)
• Typos, abbreviations and half-finished sentences

The same is true of any AI feature that takes free input, whether that is a mood description, a mock-interview answer or a creator’s metadata. If you want to see this gap for yourself quickly, deploying a production AI chatbot in 30 minutes and then watching real traffic hit it teaches the lesson faster than internal QA.

The fix: input fuzzing and graceful degradation

Two practices cover most of it:

1. Input fuzzing. Before launch, generate a large set of adversarial inputs (a hundred or more): ambiguous queries, typos, empty strings, injection attempts, non-English text. The AI does not need to handle all of them perfectly, but it needs to fail gracefully on all of them.

2. A three-tier response system. Every AI feature should have three kinds of response:

   • Confident: the AI is sure of the output, so show it directly.
   • Uncertain: the AI has a result but low confidence, so show it with a caveat (“I read your question as X. Is that right?”).
   • Failure: the AI cannot produce a useful result, so show a helpful error with an example of a question that works.

Lesson 3: Every AI product needs a fallback. No exceptions.

The Venting Spot calls an OpenAI model as part of matching users with listeners. So what should happen when the API is down, slow, or returns something malformed?

If the answer is “the user sees an error,” the design has failed. Someone who is stressed or lonely does not want to see “Service temporarily unavailable.”

Every AI feature needs a fallback that gives a degraded but working experience when the AI is unavailable:

AI Feature Fallback Pattern

User Request
     |
     v
  AI Service Call (with timeout)
     |
  +--+--+
  |     |
  OK    Fail/Timeout/Bad Response
  |     |
  v     v
AI Result   Fallback Logic
  |         |
  |     +---+---+
  |     |       |
  |   Cached    Rule-based
  |   Result    Default
  |     |       |
  v     v       v
  Show Result (with confidence indicator if fallback)

Here is what a sensible fallback looks like for each of the four AI products:

• The Venting Spot: match on availability and the selected mood alone. Less precise, but the user still reaches a person.
• QueryLytic: offer a structured query builder with dropdowns and filters, so the user can still get their data.
• AI Interview: fall back to a pre-written bank of questions by topic and difficulty, so the practice session continues.
• Lore: let the creator type titles, descriptions and license terms by hand. The AI tools help. They are not required.

The pattern is always the same. AI gives the best experience, the fallback gives a good-enough one, and total failure is never acceptable. A related failure is the model answering confidently with something wrong instead of failing outright, which we cover in mitigating LLM hallucination in production.

Lesson 4: Data privacy is not a feature. It is a dealbreaker.

The Venting Spot is where this is sharpest. It handles mental health conversations, and its design rests on two privacy decisions: no real names are required, and messaging is end-to-end encrypted. It also uses an OpenAI model for matching. That combination raises questions most product teams never ask:

• Does user emotional data leave our infrastructure? Anything sent to a model API is processed by a third party. Does the user know that? Have they agreed to it?
• Is the AI request stored somewhere? Every API provider has its own retention and logging policy. Read it, and design as if requests may be kept.
• Can the AI be used to re-identify anonymous users? If enough context about one person passes through the model across sessions, anonymity gets weaker.

The answer is to send the model as little as it needs. For matching, that may be a mood category and some tags, not the user’s own words.

The broader lesson applies to every product that sends data to an AI API:

Privacy consideration	Naive approach	Better approach
Data sent to AI APIs	Send raw user data	Strip PII and send only what the task needs
AI request logging	Rely on the provider’s policy	Keep your own logs with your own retention rules
User consent for AI features	Bury it in the Terms of Service	Clear disclosure: “This feature uses AI. Here is what is processed.”
Data residency	Use whatever region the API defaults to	Choose the processing region where the provider allows it
Right to deletion	”We will figure it out later”	Build deletion workflows from day one

In health and wellness, getting this wrong hurts vulnerable people, and no PR statement undoes it. The same requirements belong in the AI-specific contract clauses you put in front of a client, whatever their industry.

Lesson 5: AI API costs at scale are not linear

AI API costs do not scale with the number of users. They scale with usage patterns, and usage patterns are hard to predict.

A simple estimate multiplies users by requests per user by tokens per request. Each of those three numbers tends to be wrong in the same direction:

Illustration (hypothetical numbers)

Estimate:
  200 users x 3 requests/day x 1,000 tokens = 600K tokens/day

What real usage can look like:
  185 regular users x 7 requests/day x 1,200 tokens = 1,554K
   15 power users  x 25 requests/day x 1,800 tokens =   675K
                                                      -------
                                                      2,229K tokens/day

Same user count. Close to four times the tokens.

Real users write longer inputs than testers, they come back more often once a feature is useful, and a small group of power users can generate a large share of the traffic. That is the familiar 80/20 pattern, but it hurts more when every request costs money. Before committing to a budget, walk the feature through the true cost of AI features in production.

How to keep costs in check

1. Per-user rate limits on AI features. Not as a punishment, as a design constraint. “You have 20 AI queries per day on the free tier” ties cost to value.

2. Token budgets per request. Set a maximum context length for each AI call. If a user pastes in a huge input, truncate or summarize it rather than sending all of it.

3. Response caching. If two people ask the same question, the second one should hit a cache, not the API. Semantic caching (matching by meaning rather than exact text) catches more repeats but is harder to build. The patterns are covered in caching LLM responses.

4. Model selection per feature. Not every task needs a frontier model. Classification and extraction often run fine on smaller, much cheaper models, with the large models kept for hard generation.

5. Cost monitoring from the first call. A page showing daily API spend, a per-feature breakdown and trend lines means you see a spike the day it happens, not on the monthly invoice.

Cost strategy	Where it helps most	Implementation effort
Response caching	Repeated or similar questions	Medium (semantic matching is non-trivial)
Model tier selection	Simple tasks running on expensive models	Low (swap the model ID)
Token budget enforcement	Long or pasted inputs	Low (truncation logic)
Per-user rate limiting	Power-user heavy traffic	Low (rate limiter middleware)
Batch processing	Features that do not need instant answers	Medium (queue architecture)

Lesson 6: The AI loading state is a UX problem nobody has solved well

A database query that returns in a fraction of a second barely needs a loading state. An AI call that takes several seconds does, and those seconds feel long.

What does not work

Generic spinners. A spinner that runs for five seconds gives the user no idea what is happening and no reason to believe it will finish.

“Thinking…” text. Slightly better than a spinner, but it still says nothing useful.

Fake progress bars. Bars that do not track real progress (the kind that jump from 20% to 90% when the response lands) teach users to distrust your interface.

What works

Streaming responses. For text generation, such as Lore’s description writer or AI Interview’s questions, stream the response as it is generated. Content starts appearing almost at once, the user can start reading before generation finishes, and the experience feels collaborative rather than transactional.

Contextual skeletons and status messages. For features that cannot stream, like a match result on The Venting Spot, show a skeleton of the result and short messages that describe real steps (“Finding listeners who are online…”). The messages must match what the system is actually doing, or you are back to a fake progress bar.

Precomputation. For predictable AI tasks, compute the result before the user asks. If the next step is obvious, start it in the background so the answer is ready when they tap.

The loading state decision tree

Is the AI response streamable (text generation)?
  |
  +-- Yes --> Stream tokens. Show text appearing in real time.
  |
  +-- No --> Is the expected wait short (around a second or two)?
              |
              +-- Yes --> Simple skeleton loader.
              |
              +-- No --> Is the task decomposable into visible steps?
                          |
                          +-- Yes --> Show step-by-step progress messages.
                          |
                          +-- No --> Is the result predictable/cacheable?
                                    |
                                    +-- Yes --> Precompute. Show instant.
                                    |
                                    +-- No --> Skeleton + contextual message
                                              + "This usually takes X seconds"

Lesson 7: When to use a model API, a custom model, or rules

There are three ways to build the “smart” part of a feature. Each wins in different conditions.

Use a hosted model API (OpenAI, Anthropic) when:

• The task is general purpose. Text generation, summarization and classification across broad domains are what frontier models are best at.
• You need to ship fast. An API integration takes days. A custom model takes weeks to months.
• The task changes often. Prompts are easier to update than a trained model.
• Good-but-imperfect accuracy is acceptable, and you have a fallback for the misses.

Where we use it: all four AI products in this list put an OpenAI model at the center: The Venting Spot (mood-based matching), QueryLytic (plain English to database queries), AI Interview (mock interviews and feedback) and Lore (title generator, description writer and license advisor).

Use a custom or fine-tuned model when:

• You need domain accuracy that prompting cannot reach. General models do not know your vocabulary, edge cases or business rules.
• Latency matters. A self-hosted model can be faster than a network call to an API.
• Volume is high. At large scale, a self-hosted model can cost less than per-token pricing.
• Data cannot leave your infrastructure. Then you need a model you control.

All four AI products here are built on OpenAI models, and a hosted model with careful prompts is where I would start any new one. Prompt tuning is a middle ground: it customizes behavior without training anything. If you are choosing between these for a new project, our RAG vs. fine-tuning vs. prompt engineering framework walks through the decision and the cost and maintenance tradeoffs of each.

Use a rule-based system when:

• The logic is deterministic. If X, then Y. No probability, no ambiguity.
• Explainability is required. Rules can explain themselves: “We matched you with this listener because they specialize in workplace stress and are online now.” A model’s reasoning is opaque.
• The edge cases are known. If you can list every case, rules are simpler and more reliable.
• Cost must be near zero. Rules cost nothing per execution. API calls do not.

Decision factor	Hosted API	Custom model	Rule-based
Implementation time	Days	Weeks to months	Days
Per-request cost	Pay per token	Hosting cost only	Effectively zero
Accuracy on general tasks	High	Higher in its domain, with training data	Exact on known cases
Accuracy on edge cases	Variable	Better if trained on them	None unless coded
Flexibility	High (change the prompt)	Medium (retrain)	Low (rewrite rules)
Explainability	Low	Low to medium	High
Data privacy	Data leaves your infrastructure	Data stays	Data stays

Lesson 8: AI products need monitoring that traditional products do not

Traditional monitoring tracks uptime, latency, error rates and resource usage. AI products need all of that plus a layer it does not cover.

Model performance monitoring

An AI feature’s quality can fall without any traditional metric changing. Uptime is fine. Latency is normal. Error rate is zero. The answers are just worse.

That happens because:

• Input distribution shifts. Users start asking different kinds of questions than the ones you tuned for.
• Providers update models. OpenAI and Anthropic update their models, and an update that improves things overall can still make your specific prompts and few-shot examples less effective.
• Context goes stale. Prompts that reference “current” information age.

A text-to-SQL tool like QueryLytic is exposed to all three: the schema changes, people’s questions change, and a new model version can respond differently to the same prompt. Without monitoring, you find out when users complain. The tools that catch this are LLM specific: LLM observability platforms like Langfuse and Helicone for tracing production calls, and evaluation tooling like LangSmith and Braintrust for catching accuracy regressions before they reach every user.

What to monitor on an AI product

AI Product Monitoring Stack

Standard metrics (same as any web app):
  - Uptime
  - Response latency (p50, p95, p99)
  - Error rate
  - Request volume

AI-specific metrics:
  - Response quality score (sampled human evaluation)
  - Token usage per request (cost proxy)
  - Fallback trigger rate (how often does the AI fail?)
  - User satisfaction signals (did the user retry? accept the result?)
  - Model version tracking (detect provider updates)
  - Prompt template version (which prompt is in production)

Cost metrics:
  - Daily API spend
  - Cost per user per day
  - Cost per feature per day
  - Projected monthly spend (from the trailing 7-day trend)

Lesson 9: Non-AI decisions make or break AI products

This one runs against intuition. Whether an AI product succeeds depends more on its non-AI decisions than on the AI.

The Venting Spot’s strengths are mostly not the matching model. Look at what the platform is built on:

• Listeners who are background-checked, trained and verified
• Affordable pricing, with peer listener sessions at Rs.120/hr
• 24/7 availability, with 500+ listeners online
• End-to-end encryption and no real names, so anonymity is real

The AI matching makes the experience better. But if the listeners were unverified or the pricing were opaque, no amount of matching would save it.

Lore is the same story. It is a Web3 platform where creators register, protect and monetize intellectual property. The parts that carry it are the blockchain integration (IP registration on Story Protocol, minting to Ethereum), Sign In With Ethereum, royalty distribution through smart contracts, and a creator dashboard for assets, derivative works and royalty earnings. Lore’s results include 12,500+ registered IP assets and $2.4M+ in total transaction volume. The AI tools (title generator, description writer, license advisor) cut friction. The IP protection and monetization are the reason to be there.

StickGuard has no AI component to lean on. Its value is in security fundamentals: JWT-based authentication with MFA, role-based access control, real-time monitoring, automated anomaly detection with alerting, and audit logging for compliance.

The lesson: build the product first. Add AI to make it better. If the product does not work without AI, adding AI will not save it.

Lesson 10: The best AI features are the ones users do not notice

The best AI features stay out of sight.

On The Venting Spot, the user picks a mood and gets a listener. They never see a “matching engine.” The AI is behind the curtain.

On QueryLytic, the user types a question in English and gets results. The translation into a database query happens without them thinking about it.

The worst AI features draw attention to themselves: “Powered by AI!” badges, robot animations on loading states, disclaimers about “our advanced machine learning model.” Those features say “look, we are using AI.” The best ones say nothing. They just work.

AI Feature Visibility Spectrum

Worst                                                     Best
  |                                                         |
  v                                                         v
"AI-Powered!"     "Generating      "Loading..."    Just shows
badge, robot      with our AI                      the result.
animation,        engine..."                       User does not
explanation of                                     know or care
how the model                                      that AI is
works                                              involved.

There is one exception: when the AI is the product. AI Interview is a mock interview platform, so practicing with an AI is the whole point, available 24/7 with no scheduling. In that case, say so plainly. Even then, the goal is for the AI to feel natural, not like a tech demo.

Lesson 11: Scope creep hits differently on AI projects

Every software project has scope creep. AI projects have a particular kind that I call “accuracy creep.”

It goes like this. The AI feature works well enough to demo, and the client asks for it to be a bit more accurate. You spend time on prompts, context and edge cases, and it gets better. The client asks for a bit more. The next improvement takes longer than the last one, and the one after that takes longer still.

That is a diminishing returns curve, and people who have not built AI features before tend not to expect it. They assume accuracy improves at a steady rate, so the next step should cost what the last one did.

How to handle it: discuss the accuracy-effort curve at kickoff, not mid-project.

Accuracy vs. Effort (typical shape)

Accuracy
  ^
  |                                   * * *  (long tail: slow gains)
  |                        *    *
  |                 *
  |          *                  (middle: steady but slower)
  |     *
  |  *                          (start: fast gains)
  +---------------------------------------------> Effort

Set the expectation explicitly. Early accuracy comes quickly. Each later improvement costs more than the one before. Agree on the accuracy level that is acceptable for launch, launch there with fallbacks for the misses, and improve after. This is a specific case of a general problem, and our playbook for protecting a project from scope creep covers the same conversation for non-AI requests.

Bonus: one lesson from each project

Here is the main takeaway from each of the 12:

Project	Industry	Key lesson
The Venting Spot	Healthcare/Wellness	Anonymity and end-to-end encryption have to shape the architecture from the start, not be added later.
Colleatz	Food delivery	Real-time order tracking over WebSockets, plus Twilio SMS updates, is a core feature, so plan for flaky mobile connections.
AI Interview	Career tech	When the AI is the product, 24/7 availability with no scheduling is a large part of the value.
Lore	Web3	AI content tools remove the blank-field problem: four title suggestions in 30 seconds and a 150+ word description cut content creation time from hours to seconds.
QueryLytic	Data analytics	Validate every generated query before it runs. An LLM’s output is a draft, not a command.
StickGuard	Security	Security products are judged on fundamentals: MFA, role-based access, monitoring and audit trails.
Luxury Lodgings	Hospitality	Not every project needs AI. A clean, fast, well-designed site with clear room and amenity information does the job.
Parivartan Samiti	Non-profit	For an organization with 25+ years of work across 500+ villages, information architecture matters more than technology choice.
Sarmistha’s Cloud Kitchen	Food service	WhatsApp ordering can beat a custom ordering system for a small business. Meet users where they already are.
Plantree	Lifestyle/Community	Useful content (a plant database and care guides) gives people a reason to come back while the community grows.
Glassfolio	Creative/Portfolio	A portfolio is itself a design sample. Glassmorphism, GSAP animation, lazy loading and structured data all serve the same goal.
Excellence Healthcare	Healthcare	Trust signals (doctor profiles, credentials, clear booking) are the design priority for a medical site.

The meta-patterns

Three patterns run through all of the lessons.

AI amplifies everything

AI amplifies good product decisions and bad ones. A well-designed product with AI becomes more useful. A poorly designed one with AI becomes more confusing. AI is a multiplier, not a foundation.

The hard problems are not technical

The hard questions on an AI feature are rarely “how do we get the model to work?” They are:

• How do we handle failure gracefully?
• How do we protect user privacy?
• How do we keep costs under control at scale?
• How do we set expectations about accuracy?
• How do we design UX for uncertain, probabilistic outputs?

Those are product, UX and business problems. The model is the easy part.

The lessons travel

The privacy rules that matter on a mental health platform matter just as much on a database tool that holds a company’s data. The fallback thinking that protects a distressed user on The Venting Spot is the same thinking that keeps a QueryLytic user from hitting a dead end. A lesson learned on one project is worth writing down so the next one starts from it, which is why a structured post-mortem after every project beats leaving the lessons in people’s heads.

What I would do differently

If I could start these projects over:

1. Build the fallback first, then the AI feature. The fallback is the foundation. The AI is the enhancement.

2. Set accuracy expectations in the proposal, not during development. Put the accuracy-effort curve in the kickoff document, the same document that should already reflect how to price AI-assisted development work.

3. Set up cost monitoring before the first AI API call. Not after the first surprise bill.

4. Fuzz inputs during development, not after the first production incident. Build the adversarial input set before writing the AI integration.

5. Hire for product thinking, not only technical skill. The best AI features come from people who understand users, not just models.

None of this needs a research lab. It needs the discipline to treat AI as one component of a product, with the same care for failure, privacy, cost and UX as everything else. If you are building an AI product in 2026, I hope a few of these save you some time.

---

Anurag Verma is the Founder and CEO of CODERCOPS. If you are building something with AI, we should compare notes: codercops.com