Contact us and we hand you the whole platform for a full day. Every module, every agent, live data flows — test-drive the full version and see for yourself what it does to a business.
And here's the part nobody else does: no username. No password. Where other companies make you type credentials, we send you one secure link with your access embedded inside — cryptographically signed, fully automated, one click and you're in. It's the same technology behind our mTok quick-payment links. And it locks itself when your 24 hours are up.
These are direct client case studies — 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).
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.
We build what our clients need. The whole platform gets better. Everyone wins.
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 in doing so, 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. That is why he ranks fourth.
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 theHash 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 theTag 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 |
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.
