Turn Practical AI Opportunities Into Production-Ready Systems.
From AI strategy and proof of concept to agents, intelligent applications, automation, model integration, evaluation, security, and production operations—we engineer AI systems around real business problems.
Architecture
Where are you with AI?
Start With the Problem You're Trying to Solve.
I Want to Explore AI
Identify where AI can create meaningful value and determine what should be tested first.
Discuss Discovery WorkshopAn AI Model Is Only One Part of the System.
Production AI requires more than connecting an application to a model. Data, retrieval, business rules, user experience, permissions, tool access, evaluation, security, observability, and cost all affect whether the system works reliably in the real world.
AI Demo
FragileProduction AI System
ResilientFind the AI Use Case Before You Build the AI.
We evaluate the business problem, workflow, available data, technical feasibility, risk, expected value, and operational constraints before recommending an AI solution. Our goal is to reduce uncertainty, prioritize the strongest opportunities, and help you make informed technical decisions.
Discovery Flow
Prove the Idea Before Scaling the Investment.
A proof of concept answers critical questions before a full production build: Can the AI actually perform the task? Is the data usable? Which architecture works? What are the quality, latency, security, and cost tradeoffs?
Evaluated Dimensions
PRODUCT
Build AI Into the Product, Not Around It.
A standalone AI feature is a novelty. A true AI product embeds intelligence deep inside the workflows that users already rely on. AI should fit naturally into the product experience and business logic.
Generative AI for Real Product Workflows.
Beyond simple chat interfaces. We build intelligent search, document intelligence, classification pipelines, and in-product copilots tailored specifically for your business domain.
Reduces ticket volume by understanding specific customer histories and product documentation.
Turns unstructured PDFs and contracts into structured insights usable by other systems.
Retrieves process documentation and past resolutions to accelerate internal velocity.
Adds a natural language interface to complex analytics or configuration workflows.
RAG / KNOWLEDGE AI
Connect AI to the Knowledge Your Business Actually Owns.
Retrieval-augmented systems can provide models with relevant, current business context while respecting the access rules around that information.
Enterprise Retrieval Pipeline
AGENTIC AI
AI Agents That Can Do More Than Answer.
Agentic systems can plan tasks, use tools, retrieve information, interact with APIs, and carry out defined actions. The engineering challenge is making those actions controlled, observable, and appropriate to the risk involved.
Agent Workflow
Give AI the Right Access—And No More.
An AI agent should only have the permissions, tools, and actions required for its task. We design least-privilege architectures so your system is safe by default.
Machine Learning
Predict, Classify, Recommend, and Detect.
Computer Vision
Turn Visual Data Into Useful Signals.
OCR, extraction, image classification, object detection, and visual inspection built for operational scale.
NLP & Speech
Understand Text, Voice, and Language.
Semantic search, text summarization, classification pipelines, translation, and sophisticated speech-to-text processing.
Add AI to Software You Already Have.
Put AI where work already happens. Whether it's a custom SaaS product, internal ERP, legacy CRM, or workflow automation—AI becomes exponentially more useful when connected directly to your existing systems.
Existing System
CRM, ERP, Portals, SaaS
AI Layer
Agents, RAG, Automation, Search
Data Pipeline
AI Is Only as Useful as the Data Behind It.
Before an AI model can generate value, your data must be accessible, clean, structured, and securely governed. We build the data engineering pipelines that make AI systems reliable.
Choose the Right Model for the Job.
We don't assume one model, provider, or architecture is right for every use case. We align model strategy directly with your technical constraints and business goals.
Constraints
Model Strategies
Hosted Foundation Models
High capability, zero management, variable cost via APIs (OpenAI, Anthropic).
Open-Source & Local Models
Fixed cost, total data privacy, deployed on custom infrastructure (Llama, Mistral).
Fine-Tuned & Task-Specific
Smaller models tuned on internal data for low latency and high accuracy on specific tasks.
Don't Ask Whether the AI Works. Define How You'll Know.
AI systems need measurable evaluation pipelines. "It looks good" is not a production metric. AI evaluation should evolve continually as the product evolves, preventing regressions when models or prompts change.
Adversarial Pipeline Inputs
Test the System for Real-World Failure.
Conventional QA is not sufficient for generative systems. We build testing frameworks that challenge the models with unexpected data, conflicting instructions, and boundary conditions.
AI Security Requires More Than Application Security.
We design architectures that assume models can be manipulated via prompts. Data isolation, output validation, authorization, and restricted tool access are non-negotiable foundations structure.
Input Guardrails
Defending against prompt injection, malicious payloads, and context window manipulation.
Output Validation
Programmatically verifying schemas, restricting executable outputs, and scanning for PII leakage.
Vector Store Security
Enforcing tenant isolation and role-based access control inside the retrieval indices.
Action Control
Applying strict least-privilege policies to agentic API tool calling capabilities.
Build AI People Can Understand and Trust.
Good AI UX makes advanced capability understandable and controllable. An intelligent interface isn't just a chat box—it's source citations, confidence signals, suggested actions, and undo capabilities.
Automate the Work. Keep Humans in Control.
ASSIST
AI recommends or summarizes information for the user.
AUGMENT
AI completes part of the workflow and readies it for human review.
ACT
AI performs defined actions independently under controlled permissions.
Production AI Needs Production Infrastructure.
Launch is where AI engineering becomes operations. We build the ML/LLMOps pipelines required to monitor quality, observe latency, control costs, and release non-breaking updates to production software.
LLMOps / MLOps
AI needs an operating system after launch. We implement model versioning, prompt management, dataset versioning, automated evaluation pipelines, and regression testing for continuous delivery.
AI Observability
Traditional monitoring tracks requests and errors. AI observability tracks prompt inputs, retrieved context, tool calls, token usage, groundedness scores, and semantic drift.
Cost Engineering
Make AI economics part of the architecture. Optimize context size, implement semantic caching, route to smaller models for specific tasks, and monitor token usage limits.
Performance Focus
AI should feel useful, not just intelligent. We implement streaming architectures, background processing algorithms, and predictive routing to minimize user wait times.
AI Modernization
Already have AI? Make it more reliable. We audit legacy implementations, upgrade prompt pipelines, secure architectures, and add production observability to old PoCs.
Delivery Framework
How We Engineer AI.
AI projects fail when they are treated like typical software engineering, or worse, open-ended research. We apply a rigid, risk-adjusted engineering methodology that forces early validation.
Expected Outcomes
1. Strategy & Readiness
We define the exact business problem, assess data quality, and map the technical risk profile before touching a model.
2. Secure PoC
We build a confined, end-to-end slice of the system to prove the hardest technical hypotheses (usually retrieval quality or latency).
3. Engineering & Integration
We scale the architecture, integrate with your existing systems, build the data pipelines, and lock down security.
4. Extensive Evaluation
We don't trust 'vibes'. We run the system through automated test sets, adversarial edge cases, and human-in-the-loop QA.
5. Production & Ops
Launch is day one. We hand over a system wired for observability, cost tracking, and continuous improvement.
Work that solves real problems.
Explore how we approach complex product, engineering, and technology challenges.
Problem
Legacy financial systems struggled with real-time transaction processing, leading to high latency and data inconsistencies during peak market hours.
What We Built
Migrated core infrastructure to a cloud-native microservices architecture on AWS, using Kafka for event streaming and Next.js for a high-performance trader dashboard.
Key Outcomes
Problem
Clinic staff spent 40% of their time on manual patient onboarding and insurance verification, causing severe operational bottlenecks.
What We Built
Engineered a HIPAA-compliant digital patient portal with automated OCR-based insurance verification and real-time EHR integrations.
Key Outcomes
Problem
Lack of real-time visibility into supply chain routing led to massive fuel inefficiencies and delayed shipments across international borders.
What We Built
Built a predictive AI routing engine that processes live traffic, weather data, and fleet telemetry, with a fully native iOS and Android driver app.
Key Outcomes
Ready to build?
Let's discuss how we can turn your business problem into a powerful software product.
Validate Before You Build: Our Founder-First Methodology
AI Dev Planner helps founders move from an idea toward a validated and practical SaaS product plan through market research, validation, MVP planning, technical planning, cost estimation, and launch preparation.
Market Research
We analyze your target market to ensure your core idea solves a real problem before you spend capital on engineering.
MVP Planning
We map out exactly which features are required to test your core value proposition effectively without scope creep.
Technical Planning
Our architects design scalable, secure, and cost-effective technical foundations prepared for future growth.
Cost Estimation
Receive transparent, highly accurate cost projections so you can manage your runway efficiently.
Idea Validation
Test product viability quickly. Validating reduces risk and ensures you build something the market actually wants.
Launch Preparation
Deploy with confidence. We align technical readiness with your initial go-to-market strategy.
How does AI Dev Planner reduce software costs?
By shifting the focus heavily onto pre-development validation and technical planning, we identify expensive edge-cases, eliminate unnecessary feature bloat, and align the architecture with reality rather than assumptions.