Adaptive and collaborative AI
Corrections stay in the workspace. Mistakes do not reset.
Team learning receipt
3 services · 11 files · 0 shared schemas
Checks green. Staging URL live. Spend recorded on the same thread.
The category
Stateless assistants forget every correction. Arvad keeps conventions, review feedback, and prior build records in one workspace so the next build starts from your team, not a blank prompt.
Corrections and review notes stay in the workspace
Conventions detected from the codebase, not rule files
Org-wide patterns shared without copy-paste configs
Prior build records inform the next pass
The gap
Most assistants treat every session as a blank slate. They miss your conventions and force the team to re-teach the same lessons.
66%
Stack Overflow's survey of 90,000+ developers found the #1 frustration with AI tools is code that's close but doesn't match team patterns, requiring constant manual correction that erodes productivity.
Stack Overflow Developer Survey, 202467%
Harness's 2025 report found 67% of developers spend more time debugging AI-generated code than they would writing it themselves. 59% experience deployment errors at least half the time when using AI tools.
Harness State of Software Delivery, 202565%
Qodo's 2025 study found 65% of developers using AI for refactoring say the assistant "misses relevant context." The #1 improvement request (26% of all votes) is better contextual understanding, not better models.
Qodo State of AI Code Quality, 202519%
The METR randomized trial found experienced open-source developers using AI tools were 19% slower, yet believed they were 20% faster. Generic AI helps juniors but actively hurts the seniors who know the codebase best.
METR RCT, July 2025Static rule files (.cursorrules, instruction docs) break at scale. Adaptive context needs something better.
1,000
Line Limit on Copilot Instructions
GitHub's own docs acknowledge "very long instruction files may result in some instructions being overlooked." Org-level instructions cap at 4,000 characters.
GitHub Docs0
Tools That Learn From Corrections
Major assistants still reset across sessions.
15K
Lines Before Cursor Struggles
Abnormal AI documented that writing rules for "thousands of files in our monorepo" was infeasible, they had to build a custom LLM to auto-generate rules. Cursor freezes on 400K+ file codebases.
Abnormal AI Engineering Blog$8M
Annual Productivity Loss per 250 Devs
Harness estimates organizations lose $8 million per 250 developers annually from AI-related inefficiencies, debugging, rework, and context re-establishment that adaptive AI would eliminate.
Harness, 2025Most tools rely on manual configuration. Arvad learns from how your team ships.
Arvad sits where the thread keeps the plan, staging, spend, and checks.
| Capability | Static rules (Cursor / Copilot) | Arvad |
|---|---|---|
| How AI Learns Your Patterns | Manual .cursorrules / .md files | Learns from every interaction automatically |
| Team Knowledge Sharing | Copy-paste rule files across repos | Org-wide learning graph, shared across all devs |
| When AI Repeats a Mistake | Dev corrects it again next session | Correction stays available for later builds |
| New Developer Onboarding | 3-6 months to learn conventions | AI teaches team patterns from day one |
| Codebase Context | 64K-200K token window (partial view) | Full codebase + history + conventions |
| Maintaining AI Instructions | Manual updates every sprint | Less manual rule churn |
| Code Review Learnings | Lost after PR is merged | Captured and applied to future generations |
| Acceptance Rate | ~30% (industry average) | 45%+ with adaptive context |
| Architecture Awareness | Single-file suggestions | Cross-repo dependency understanding |
| Scaling to 100+ Developers | Rules diverge across teams | Unified org intelligence, team-specific tuning |
Arvad builds an evolving picture of your conventions and decisions. Every interaction can improve the next build.
Corrections, reviews, and architecture decisions stay available so the whole team benefits.
Stays in the workspace with plan and checks
Detects naming, architecture, and API preferences from the codebase, then applies them.
Stays in the workspace with plan and checks
Team-level context with org-wide patterns shared where they help.
Stays in the workspace with plan and checks
Learning signals from generation, correction, and review.
Stays in the workspace with plan and checks
Works across repos when context exceeds a single window.
Stays in the workspace with plan and checks
You still steer standards; adaptive memory does not remove review.
Stays in the workspace with plan and checks
Patterns stay when people leave.
Stays in the workspace with plan and checks
Security patterns learned from prior reviews and scans.
Stays in the workspace with plan and checks
2024–2025 research keeps pointing at organizational context gaps.
2,500%
Predicted Defect Increase
Gartner predicts prompt-to-app approaches will increase software defects by 2,500%, driven by "context-deficient code" that lacks awareness of system architecture and business rules.
Gartner Predicts 20267.2%
Delivery Stability Drop
Google DORA 2024 found AI adoption correlates with decreased delivery stability, because AI generates code faster than organizations can ensure quality.
DORA Report, 202430%
Don't Trust AI Output
DORA 2025 found 30% of developers trust AI "a little" or "not at all", even with 90% adoption. Trust requires AI that understands your conventions, not just syntax.
DORA Report, 2025$31.5B
Lost to Knowledge Silos Annually
Fortune 500 companies lose $31.5B/year from failing to share knowledge. Developers change jobs every 2-3 years, taking institutional knowledge with them.
From first codebase analysis to patterns that improve later builds.
From first codebase analysis to patterns that improve later builds.
Static context without persistent learning. Data below.
“Copilot excels at single-file tasks but faces challenges with cross-repository architectural understanding.”
“Abnormal AI found writing rules for thousands of files in a monorepo was infeasible, they had to build a custom LLM to auto-generate them.”
“Reviewers note "very generic answers when working with domain-specific or highly customized architectures."”
“Even Tabnine now argues against fine-tuning, calling it something that "at worst, bloats the model and makes future updates brittle."”
“Cody excels at explaining code and context-aware suggestions but lacks agency for multi-file operations or learning from team interactions.”
“The agent will not learn as it goes. Every time you reset the context, you're working with another brand new hire.”
Research on adapted models and RAG shows large deltas; these are third-party figures, not Arvad SLAs.
80%
More AI-Written Code After Fine-Tuning
Refact.ai found that after adapting a model to a proprietary codebase, AI-written code share jumped from 25% to 45%, an 80% relative improvement in code the team actually ships.
Refact.ai, 20241.4×
Acceptance Rate Improvement
Databricks' Never Ending Learning research showed that using interaction data (acceptances, edits, rejections) as learning signals produced 1.4× better acceptance than GPT-4o, at no annotation cost.
Databricks NEL, 2024150×
Cheaper With Adaptive RAG
Together AI showed RAG-fine-tuned Mistral 7B matched GPT-4o and Claude 3 Opus on 4 of 5 codebases while being 150× cheaper and 3.7× faster at inference.
Together AI Research25%
Competitive Outperformance
Gartner projects that by 2026, businesses implementing adaptive AI will outperform competitors by 25%. The $1.04B adaptive AI market is projected to reach $30.5B by 2034.
Solo or team. Upgrade when usage grows.
Compare plansPick the plan that matches how you build. | FreeStart free | StarterChoose plan | ProfessionalChoose plan | EnterpriseChoose plan |
|---|---|---|---|---|
| Credits included | 120 credits included | 240 credits Included | 500 credits Included | |
| Projects per week | 1 mini project | 2 mini projects | 4 mini + 2 medium projects | |
| Documentation access | Essential | Standard | Full | |
| Planning & task breakdown | ||||
| Dependency resolution | ||||
| Queue-based processing | ||||
| Daily questions | Limited | Up to 10 / per day | Up to 25 / per day | |
| Upgrade options | ---- | Large projects | Large-scale projects | |
| Price | $0.0 | $25 | $50 |
Claims cite published research. Read the sources.
609 developers surveyed. 65% say AI misses context in refactoring. #1 improvement request: contextual understanding.
49,009 developers. Only 33% trust AI accuracy. Positive sentiment dropped to 60% from 70%+.
500 engineering leaders. 67% debug AI code more than writing it. $8M annual loss per 250 devs.
Experienced OSS developers 19% slower with AI tools, yet believed they were 20% faster. Dangerous perception gap.
Fine-tuning on interaction data achieves 1.4× acceptance improvement over GPT-4o at zero annotation cost.
RAG + fine-tuning matches frontier models at 150× lower cost. Demonstrates codebase-specific adaptation.
Enterprise monorepos exceed all model context windows. Hierarchical memory and organizational context are essential.
Proposes persistent knowledge layer for codebases. Identifies "uncontrollable knowledge entropy" as the core bottleneck.
Identifies "context-deficient code" as new defect class. Predicts 2,500% defect increase from prompt-to-app.
AI amplifies existing strengths and dysfunctions. 7 capabilities model. 90% adoption but 30% trust gap.
10,000 developers, 1,255 teams. 21% more tasks but 91% longer PR reviews. Individual gains don't translate to org outcomes.
Comprehensive synthesis of RCTs, telemetry, and industry data. Covers METR, Faros, DORA, and security risks.
Limits stated plainly. No demo theater.
One question
If you cannot, you still have a chat log. Arvad keeps that work in one workspace.
Keep exploring
Corrections compound. Chat scrollback does not.