AI Consulting
Strategy · RAG · Agents · Governance
We find where AI will create real value in your company, choose the right model and put the solution we recommend into production, securely and measurably.
AI consulting means finding where AI will create real value in your company, choosing the right model and architecture, and putting it into production securely and measurably. At Next to Future, we work with companies that want to speed up processes or add AI to their products, starting from business goals and data, and we build what we recommend. The outcome is not a slide deck but a system your team uses every day, with an impact you can measure.
Who is AI consulting for?
It is most useful in situations like these:
- You want to use AI but are not sure where to start.
- Your teams search for and copy the same information across documents, emails and spreadsheets by hand.
- You want to add an AI feature to your product, but questions about quality, cost and privacy are still open.
- You have run experiments, but none reached production or had its impact measured.
- You work with personal data and need to be sure you comply with GDPR and KVKK, Türkiye's data protection law.
What does our AI consulting cover?
- Readiness and discovery: we review your processes and rank use cases by impact and feasibility.
- Data assessment: we map which data you have, where it lives and how current and accessible it is.
- Build or buy: we decide together whether an off-the-shelf tool is enough, and which model and hosting option fits.
- RAG: retrieval-augmented generation is a method where the model first finds the relevant information in your own documents and then answers from it. Assistants that work with company data are built this way.
- Agents and process automation: AI agents that use tools to carry out multi-step tasks toward a goal, and workflows that take over repetitive work.
- Product integration: search, summarization, classification and assistant features inside your web or mobile product.
- Governance and security: GDPR and KVKK compliance, access rights, logging and the steps that need human approval.
- Measurement and enablement: ROI tracked against metrics agreed up front, and training so your team uses the tools well.
Why do we build what we recommend?
When consulting and delivery sit with different people, advice that looks right on paper tends to stall in practice. We build the architecture we propose and measure its cost and quality on real data. Our own products, Next AI and Next Platform, are built on AI as well.
If the AI feature will live inside a business application, we handle the web application development side in the same project. We cover how far agents can go in autonomous AI agents and the first small steps in AI process automation for SMBs.
01 Scope
From Idea to Working System
01
Readiness and Use-Case Discovery
We review your processes and data and prioritize the use cases where AI will create real value.
02
Model and Architecture Choice
Off-the-shelf or custom, which model, which hosting? We decide by balancing quality, cost, speed and privacy.
03
RAG, Agents and Integration
Assistants that work with your company data, agents that run processes and AI features built into your product.
04
Governance, Security and Measurement
GDPR and KVKK compliance, access rules, ROI tracking and training so your team uses the tools well.
02 Process
From Discovery to Production
01
Discovery
We review your goals, processes and data. You get a prioritized list of use cases and a short data readiness summary.
02
Design
We set the model, architecture, security rules and success metrics. You get a clearly scoped roadmap and estimate.
03
Pilot
We build the chosen use case on real data at small scale and measure quality and cost, so the next decision rests on data.
04
Rollout
We put the solution into production, monitor it and train your team, reporting results regularly against the original metrics.
03 Technology
The Right Model for the Job
We are not tied to a single model or provider. For each use case we choose by balancing quality, cost, response time and data privacy.
01
Models and APIs
Cloud models through APIs such as OpenAI and Anthropic, or open models running on your own infrastructure when data must stay in-house.
02
Data and Retrieval Layer
We chunk and index documents in a vector database and build RAG pipelines that cite their sources and respect permissions.
03
Automation and Integration
Workflows in n8n, APIs in .NET and Node.js, and deployment on Docker and Kubernetes that is observable and scales.
- 01OpenAI API
- 02Anthropic API
- 03vector databases
- 04n8n
- 05ComfyUI
- 06.NET
- 07C#
- 08Node.js
- 09TypeScript
- 10SQL Server
- 11Redis
- 12RabbitMQ
- 13Docker
- 14Kubernetes
- 15Azure
04 Proof
From Our Own Products
Products of ours that are built on AI or made to work alongside AI tools.
05 FAQ
Frequently Asked Questions
Core questions to settle before you start an AI project.
01What determines the cost of AI consulting?
Scope determines the cost. The main factors are whether you need discovery and a roadmap only or also a pilot and a production system, how ready your data is, how many systems need integrating, the usage fees of the chosen model, and your security and compliance requirements. After a short discovery call, we share a scoped estimate.
02Is our data safe, and does it go to model providers?
We decide together, at the start, where your data goes. If a cloud API is used, we review the provider's data processing and retention terms and strip or mask personal data the model does not need before sending anything. We also assess GDPR obligations and KVKK's rules on transfers abroad. If data must not leave your environment, running the model on your own infrastructure is an option.
03Should we use an off-the-shelf AI tool or build a custom solution?
If the need is common and your data is not sensitive, an off-the-shelf tool is often the fastest and most sensible route. A custom solution makes sense when your process is specific to you, needs to integrate with several of your systems or requires full control over data. In discovery we compare both on quality, cost and maintenance effort, and we recommend the ready-made tool when it fits.
04How do you measure ROI?
We set the metric at the start, before the pilot begins. First we measure the current state, such as how long a task takes, the error rate, response time or the hours your team spends. We track the same metrics in the pilot and in production and report the gains alongside model, infrastructure and maintenance costs. That way, the decision to continue or expand rests on data rather than estimates.
05When will we see first results?
First results usually show up in the pilot, and the timing depends on scope and how ready your data is. Discovery typically wraps up within a few weeks. A pilot focused on a single use case usually takes from a few weeks to a few months, while the time to production depends on integrations and security requirements. We set the schedule together after discovery.
06Do you only advise, or do you also build the solution?
We build what we recommend. After discovery and the roadmap, we build the pilot, take it to production and handle maintenance; you can also continue development with your own team if you prefer. Either way, we train your team to use the tools correctly and safely and write down clear usage guidelines. That keeps responsibility in one place, so nothing gets lost between advice and delivery.
Touch the Future
Let's Find Your First Use Case
Tell us briefly about your goal and the data you have. After a short discovery call, we will work out together where it makes sense to start.