AI Solutions
AI solutions development is the engineering of production systems that use large language models - retrieval-augmented generation, multi-agent workflows, evaluation pipelines, and model integrations that keep working after the novelty of a demo wears off.
About AI Solutions
We work with founders and technical teams in the USA, UAE, Germany, and across Europe who have already tried the off-the-shelf AI tools and hit a ceiling - a chatbot that hallucinates on their actual documents, a workflow that needs more than a single prompt, or a prototype that never survived contact with real users.
The difference between an AI demo and an AI product is almost entirely in the parts nobody photographs for a pitch deck: evaluation datasets, guardrails, cost controls, and fallback behavior when a model gives a wrong answer with total confidence. That is where most of our engineering time actually goes.
What's included
Everything we deliver on this engagement
AI product strategy - RAG, fine-tuning, or agents
We start by working out which architecture actually fits your problem, not which one is trending. Retrieval-augmented generation, fine-tuning, and multi-agent orchestration solve different problems, and picking the wrong one is the most common reason AI projects stall.
Retrieval-augmented generation with vector search
Document ingestion, chunking strategy, and vector store setup so your AI answers from your actual content instead of the model's general training data. We tune retrieval quality against real queries, not a handful of happy-path examples.
Multi-agent orchestration with tool calling
Agent workflows that call tools, query your systems, and escalate to a human when confidence is low - built with explicit failure paths, because an agent that fails silently is worse than no agent at all.
Evaluation suites and regression testing
Structured eval datasets so you can measure accuracy, catch regressions before they reach users, and prove to stakeholders that a prompt or model change actually improved things instead of just feeling different.
GPT-4, Claude, and open-model routing
Provider-agnostic integration so you are not locked into one model - with routing logic that balances quality, latency, and cost across GPT-4, Claude, and open-weight alternatives depending on the task.
Admin interfaces for prompt management and review
Internal tools for your team to review conversation logs, flag bad outputs, and iterate on prompts without touching code for every adjustment - because prompt tuning is ongoing work, not a one-time task.
RAG vs fine-tuning - when to choose each
| Factor | RAG | Fine-tuning |
|---|---|---|
| Best for | Changing knowledge bases and docs | Stable tone, format, or domain style |
| Data needs | Documents + retrieval pipeline | Curated training examples |
| Update speed | Update docs without retraining | Requires new training runs |
| Typical start | Internal search, copilots, support | Brand voice, classification, formatting |
Our process
How we deliver ai solutions
- 01
Define the AI product (weeks 1–2)
We clarify the actual job-to-be-done, success metrics, and available data sources before selecting a single model or framework - most AI project failures trace back to skipping this step.
- 02
Prototype and evaluate (weeks 2–4)
A working slice tested against an evaluation dataset built from your real content, so you see accuracy and failure modes on your actual use case instead of a curated demo.
- 03
Productionize (weeks 4–8)
APIs, authentication, rate limiting, logging, and deployment on Docker or your cloud of choice - the unglamorous engineering that determines whether the AI feature survives real traffic.
- 04
Iterate with production data (ongoing)
Weekly improvements to prompts, retrieval quality, and guardrails driven by what actually happens in production conversations, not assumptions made during the design phase.
Tech stack
Tools we use for ai solutions
GPT-4
Our default model for most production use cases where reasoning quality and tool-calling reliability matter more than raw cost.
OpenAI API
Direct integration for chat completions, embeddings, and function calling without unnecessary abstraction layers between your product and the model.
Python
The standard for inference services, evaluation scripts, and anywhere the AI ecosystem's tooling is strongest.
Node.js
Used when the AI service needs to sit close to an existing JavaScript backend or realtime infrastructure.
Next.js
Powers the customer-facing interface for AI features - chat UIs, dashboards, and admin review tools in one React codebase.
Supabase
A fast path to vector storage, auth, and structured data when you do not want to run separate infrastructure for each piece.
Docker
Keeps inference services reproducible across local development, staging, and production without dependency drift.
Who this is for
Use cases that commonly need ai solutions
Internal knowledge search that actually finds answers
Your team has years of documentation scattered across Notion, PDFs, and old wikis. We build retrieval systems that surface the right answer with a citation, instead of a generic search box that returns twenty irrelevant results.
Customer support triage before it reaches a human
An AI layer that reads incoming tickets, drafts responses grounded in your actual policies, and escalates anything it is not confident about - reducing response time without letting the model improvise on refund policy.
A multi-step agent replacing a manual research process
A workflow that used to take an analyst two hours - pulling data from multiple sources, summarizing, and flagging exceptions - reduced to a monitored agent pipeline with a human checkpoint before anything ships.
Adding AI to a product that already has real users
You have an existing Laravel or Next.js application with paying customers, and want to add an AI feature without risking the stability of what already works. We build the AI service alongside your product, not as a replacement for it.
Common mistakes
What teams get wrong about ai solutions
"We'll just fine-tune GPT on our data"
Fine-tuning is the right tool far less often than founders expect - it is expensive to iterate on and does not teach a model new facts reliably. Most 'we need fine-tuning' conversations end up solved with retrieval instead, at a fraction of the cost.
"If the demo works, the product works"
A demo tests the happy path with inputs you already know work. Production traffic will find the edge cases within a week. We budget real time for evaluation and guardrails precisely because the gap between demo and production is where most AI projects quietly fail.
"AI will replace our support team entirely"
The projects that succeed use AI to handle volume and free up humans for judgment calls, not to eliminate the team. Designing an honest escalation path is part of the engineering, not an afterthought bolted on when the model gets something wrong publicly.
"One model will handle every task in our product"
Different tasks need different tradeoffs between cost, latency, and reasoning depth. We frequently route simple classification to a cheaper model and reserve GPT-4-class reasoning for the tasks that actually need it, which also controls your monthly API bill.
Pricing & timeline
What to expect on budget and schedule
Focused AI features - a RAG-powered search or a single agent - often start at $15k to $35k. Full AI-native products with multiple workflows and evaluation infrastructure typically run $40k to $80k or more, scoped in phases so you are not committing to the full budget upfront.
A production RAG feature or internal copilot usually ships in 6–10 weeks. Multi-agent platforms with compliance considerations or multiple integrations run 12–16 weeks with staged rollouts, so early modules are live while later ones are still in development.
If you already have a Laravel or Next.js product, adding an AI feature is usually cheaper and faster than these ranges suggest, because we are extending existing auth and infrastructure rather than building a new product from zero.
FAQ
Common questions about ai solutions
How much does custom AI development cost?
- Focused AI features (RAG search, single agent) often start at $15k–$35k. Full AI-native products with multiple workflows and eval infrastructure typically run $40k–$80k+ scoped in phases.
How long does it take to build an AI product?
- A production RAG feature or internal copilot usually ships in 6–10 weeks. Multi-agent platforms with compliance needs run 12–16 weeks with staged rollouts.
Do you fine-tune models or use RAG?
- We recommend RAG first when your knowledge changes frequently. Fine-tuning makes sense for stable tone, format, or domain vocabulary. Often we combine both.
Can you integrate AI into our existing Laravel or Next.js app?
- Yes - that is most of our work. We add inference services behind your existing auth, billing, and UI rather than rebuilding everything around AI.
How do you stop an AI feature from hallucinating on our data?
- Primarily through retrieval-first design - the model answers from retrieved passages instead of memory - combined with eval datasets, confidence thresholds, and a defined escalation path for anything below that threshold. We test against adversarial questions before launch, not just the friendly ones.
Do you build AI evaluation and monitoring, or just the initial feature?
- Both, and we consider evaluation the more important half. Without an eval suite you cannot tell if a prompt change made things better or worse. We set up logging and lightweight evals as part of the initial build, not as a separate future project.
Related
Related services and work
Related services
Portfolio & reading
Ready to scope ai solutions?
Tell us about your product, timeline, and constraints. We reply within one business day with next steps - no generic pitch deck.