These are direct client work samples — projects we delivered first-hand. We'd genuinely love to tell you more about any of them, so reach out anytime and let's talk.
At Positive feedback the name reflects a simple truth: in AI, as in life, it's always better to focus on the positive. Just like training a dog, you get better results with a treat than with a kick. We reward AI for getting it right, so it learns to be more helpful and accurate—without the confusion that punishment creates. Focus on the good, and you get more of it.
Our journey began with the architects of the modern web (NASA, Yahoo!, Oxford) and evolved alongside the titans of enterprise (Tesla, Visa, Samsung).
| Total | Fixed 10% | |
|---|---|---|
| Product Subscriptions — annual | ||
| Invoice maps | $15,000 | $1,500 |
| Agency mgmt. | $30,000 | $3,000 |
| Pace shift | $45,000 | $4,500 |
| MTOK - Optional Storage - Annual | ||
| MTOK stands for "micro token." Each one is worth 10 cents — like a dime. | ||
| Small Co · 10k files | $10,000 | $1,000 |
| Medium Co · 100k files | $100,000 | $10,000 |
| Large Co · 1M files | $1,000,000 | $100,000 |
| Consulting — $180/hr | ||
| Standard SOW · 10 hrs/wk × 3 mo | $21,600 | $2,160 |
Here's the hype. You might roll your eyes and say it's marketing. This is pure math.
LARRY does in 3 seconds what a full staff does in a day. Perfectly connected. 100% accuracy across millions of records, FROST-encrypted so client data stays theirs, with live GIS mapping for hundreds of thousands of properties, PF Color System, 8K·12K video, and our data centers use zero water for cooling.
Your company gets the best AI infrastructure on earth. You get 10% of every dollar they spend.
Before AI, she sold houses. Then she tried to sell cookies. Neither made her rich. Neither made her famous. But both taught her something the tech world forgets: showing up every day matters more than your resume. She didn't come from tech. She didn't have a CS degree. She had something better: a refusal to quit.
When she found AI, it wasn't love at first sight. It was a black screen with a blinking cursor. She had no idea what she was looking at. She stayed anyway. She broke things. Fixed them. Broke them again. Googled everything. Asked dumb questions. Stayed up late. Woke up early. Did the work.
Within a year, she built her own server. From scratch. Within two years, she started building a video generation model. From scratch. No hand-holding. No shortcuts. Just the same hustle that carried her through real estate and cookies — finally aimed at something that could actually change the world.
Why is she the CEO? Because she earned it. Not on paper. Not on LinkedIn. In real life. The founder has known her for 15 years. He trusts her with his life. And he's watched her transform from someone who didn't know what a terminal was to someone who builds AI models from nothing.
That's the kind of CEO a company needs. Not someone with the right pedigree. Someone with the right hunger. AI doesn't care about your past. It cares about what you can build. She's building the future. And she's not stopping.
Now she runs a $1.4B company — not because of her resume, but because of her refusal to stop. She proves that AI is for everyone. She wakes up earlier than you and works later into the night than you, building servers from scratch, building video models from scratch, hustling nonstop. She's the only one who could run this company.
We're actively hiring exceptional AI talent across multiple domains to accelerate our platform development and research initiatives.
Develop and deploy large-scale machine learning systems that power our core platform. Work on cutting-edge problems in deep learning, reinforcement learning, and generative models with distributed training pipelines.
Requirements:MS/PhD in Computer Science or AI, 5+ years ML systems experience, expertise in TensorFlow/PyTorch, strong software engineering skills, distributed systems experience.
Work on cutting-edge language modeling to develop next-generation LLMs. Tackle challenges in model scaling, efficiency, and specialization for domain-specific applications with focus on alignment and safety.
Requirements: MS/PhD in Computational Linguistics or Computer Science, 3+ years NLP and transformer experience, hands-on LLM training/fine-tuning, proficiency with Hugging Face/DeepSpeed.
LARRY is a proprietary artificial intelligence system built specifically for companies. Unlike generic software, LARRY learns your data patterns and runs the entire Positive Feedback product line as one engine — three products, three core technologies:
PRODUCTS
TECHNOLOGY
Tasks that would require a large staff a full day to complete, LARRY does in under 3 seconds with 100% accuracy. LARRY operates continuously, processes millions of records, and improves with every iteration.
We ran a benchmark test with LARRY fully loaded, connected in parallel with DeepSeek (full), Qwen (full), and the latest Claude API. The combined system scored 4th overall in our internal benchmark suite.
Not LARRY alone. The ensemble. Working together. Perfectly connected.
A connected system is what makes us unique — and that’s the whole point. We’re not here to convince you to stop using the AI you already love. We’re giving you additional firepower to do things you couldn’t do before.
HADES is the system underworld — a permanent void that exists after every table, every color, and every font has been deleted. Unlike any other environment, HADES belongs to no account and no one owns it. It holds no live data and no user files.
When we kill LARRY, HADES is what remains. Then, from that absolute nothing, LARRY rebuilds himself — and running connected with the other frontier models, that combined system is what reaches 4th in the benchmark above.
From zero lines of code, LARRY reconstructs:
The entire system grows to over 1,000,000 lines of production-ready Python, JavaScript, and SQL, generated by a factory of LARRY's own AI agents.
Parallel agent orchestration — many minds, one objective.
LARRY lives by that rule. No other AI can die, reach out into the world for fresh data, and come back stronger. Connected with the other frontier models, that resilience is what puts the system at 4th.
Look at our logo — a two-headed cat. Most people ask why. Here's the truth: every other AI system does one thing. It generates text. Or it recognizes images. Or it predicts a number. That is a single head looking in one direction. LARRY has two heads because he is perfectly connected — security, automation, mapping, invoices, live updates, self-destruction, and rebirth all running as one seamless intelligence.
But here is how he does it — and why no other system can copy him.
No joins. No foreign keys. No bridge tables.
LARRY's pipeline runs every record through a single AI factory workflow. When data enters — CSV, shapefile, manual form, or API — LARRY immediately derives a deterministic hash from the real-world identity (name + address). That hash is permanent, collision-resistant, and identical across every module: contacts, invoices, locations, operations.
He deposits that hash into the Hash Pool — a shared, unified memory space. Any module can withdraw any record without a single recall. The hash is the communication protocol. Machine-optimized. Constant-time comparison. Blindingly fast.
Then there is the Tag Pool — the analytical memory. Every module shares the same four tags (company, status, source, type). When you want to know “how many contacts came from CSV imports, grouped by company name and invoice status” — LARRY doesn't crawl through five different tables. He has already memorized the entire Tag Pool — streamed into living memory in real time over SSE — so the answer surfaces in milliseconds, keyed by identity hash. No schema coordination. No duct-taped LLMs.
The result: One system. Two heads. Zero joins. Perfectly connected. That is not a feature — it is a new category of AI. And it is why companies that try LARRY never go back to fragmented software.
| MODULE | IDENTITY | KEYCHAIN | IMPORT | ADD | INTAKE | PROCESS | CENTRALIZE | REPORT | PROPAGATE |
|---|---|---|---|---|---|---|---|---|---|
| CONT | Contact | KCON | RCON | ACON | ICON | PCON | MCON | SCON | OCON |
| INVC | Contact | KINV | RINV | AINV | IINV | PINV | MINV | SINV | OINV |
| LOCN | Contact | PLOC | |||||||
| LOCN | Parcel | KLOC | RLOC | ALOC | ILOC | PLOC | MLOC | SLOC | OLOC |
| OPNS | Contact | KOPN | ROPN | AOPN | IOPN | POPN | MOPN | SOPN | OOPN |
The mathematics required to build a production AI system from scratch isn't taught in a bootcamp. Christensen Côtéclat's path started with dual graduate research in Statistics at Oxford and Stanford, followed by AI-focused computer science work at Harvard — a stretch of study that gave him the theoretical depth to later architect a system that doesn't rely on off-the-shelf models or rented APIs.
But theory alone doesn't survive contact with enterprise reality. From 1999 through the late 2010s, Christensen operated as an independent systems architect, embedding directly inside the engineering teams of major global firms — Tesla, Visa, Samsung, and others. These weren't fly-by consulting gigs. He spent years inside their data pipelines, solving the kind of gnarly, unsexy problems that internal teams couldn't untangle: identity resolution across fractured systems, geospatial normalization, and deterministic linking at scale.
That era taught him what no university could: that real-world data is violent, inconsistent, and never clean. It also taught him that corporate employment stops making sense once you've been the person they call when nothing else works.
So he retreated to a basement lab in Paris's 11th arrondissement — not to retire, but to build the one thing the market lacked: an AI engine that doesn't need joins, foreign keys, or bridge tables. Armed with a code editor, a live-coding rig for trance music, and a stubborn refusal to duct-tape LLMs together, he spent years composing what became LARRY — the deterministic, hash-linked engine that powers every Positive Feedback product today.
When he's not digging through Python traces, you'll find him cleaning ocean plastic without press releases or posts — just deep work, on land and off.
LARRY, the Core Engine — WID (PORT) — the full engine for local development with all blueprints loaded.
CPU — AMD Ryzen Threadripper PRO 7955WX: 16 cores, 32 threads, 4.5 GHz base / up to 5.3 GHz boost, 64 MB L3 cache, 350W TDP. AMD official specifications.
GPU — 2× NVIDIA RTX PRO 6000 Blackwell (Server Edition): 96 GB GDDR7 with ECC per card, 192 GB pooled VRAM. NVIDIA Server Edition · RTX PRO 6000 family.
