Launch Your
Enterprise AI App
We design and code optimized LLM software. High-performance vector embeddings, pgvector data stores, prompt filters/guardrails, and real-time cost trackers constructed for secure corporate operations.
Startup Reality Check: The AI Cost Avalanche.
AI applications fail when model response times drag to 8 seconds or API cost bills spiral. Run our token latency simulator to check how cached query routers protect budgets.
Redis Semantic Caching
Cache EnabledSemantic caches verify queries locally in Redis first. Matching prompts bypass the LLM server entirely.
HNSW pgvector Indices
HNSW searchHNSW spatial search indexing groups similar vectors. Retreives document contexts in milliseconds.
Fine-Tuned Small Model
Local Small ModelPrompts route to a small fine-tuned model hosted privately. Fast inferences and low hosting overhead.
Inference Latency
0.8s
Response Churn Risk
Optimized (Fast)
Document Security
Sandbox Checked
Projected API Cost
$200/mo
*Telemetry modeled using log summaries and token statistics from over 12 LLM tools hosted on our VPC clouds.
Verify Launch ReadinessAI App MVP Development Roadmap
We design and ship robust products in structured sprints. Interact with the journey pipeline steps below to view the architectural focus of each phase.
Phase 1: Token Scoping & Vector Specs
Key Features & Deliverables
- Interactive Figma layouts outlining prompts playground and usage charts
- Data chunking architecture specifying overlapping token window bounds
- API specification for embedding creation and database search queries payload
Decoupled Document Ingest & RAG Inference Flow
We design private backend infrastructures that process data locally. Hover over the nodes in our blueprint schema to inspect the file pipelines.
Document Chunking
Parses files locally into overlapping tokens blocks
Vector Embeddings
Writes vectorized coords to pgvector tables in private DBs
Semantic Cache
Redis cache intercepts matching requests in 80ms
Encrypted VPC Isolations
User files stay inside private virtual networks, keeping document context strictly isolated from model training pools.
Self-Correcting Formats
Dynamic schemas double-check LLM prompt outputs. Any invalid replies trigger self-correcting validation runs.
Active Prompt Shielding
Input guardrails intercept prompts, rejecting injection attempts and protecting system boundaries.
MVP Cost & Timeline Calculator
Configure your AI tools and features to instantly simulate budgets and estimated development sprint durations.
5 Weeks
$14,400
Cost covers Figma prompt playgrounds wireframes, pgvector database configurations, secure document sandbox setup, LangChain agent loops, and token billing ledger reviews.
Launch Readiness Assessment Quiz
Answer 5 quick conceptual questions to evaluate if your enterprise specifications are ready for development sprints.
How do you structure custom document queries?
Got Questions? We Have Answers.
Review the common engineering, costs, and data security queries AI platform founders discuss with our core development leads during scoping.
We isolate data layers. We configure private VPC parameters inside cloud services (like AWS/Supabase). Document files are parsed, converted into vector representations locally on secure middleware, and written to private SQL database instances. External LLM endpoints receive only numerical matching context packets.
We integrate semantic caching layers. When a user sends a query, we check a Redis semantic index database. If a highly similar request exists, the cached output is served instantly in milliseconds, bypassing the primary LLM model completely and saving token budget.
Absolutely. We design flexible SDK interfaces. By abstraction, you can swap out primary endpoints (OpenAI / Anthropic) with custom fine-tuned open-weights models (like Llama 3 or Mistral) running on private compute hosts, reducing operational costs by up to 80%.
We construct rigid schema validations. All agentic workflow prompts are wrapped in strict format schemas (JSON Mode / Zod validation). If an output fails validation checks, recursive self-correcting loops trigger automatically to fix the formatting.
Yes. Upon product completion and hand-off, all custom prompts sheets, database indexing scripts, langchain codes, and cloud deployment pipelines are transferred to your repository, giving you complete intellectual property rights.
Ready to Build Your AI Platform MVP?
Let's schedule a 30-minute technical scope review. We will map out your vector database indexes, review prompt caching parameters, and deliver an estimated development roadmap document.