AI Development Services
AI that ships to production, and honest advice about what you actually need. DBB Software delivers custom AI development: fine-tuned, domain-specific AI models and LLM applications built on your data, grounded in RAG, and measured with real accuracy. Applied AI, engineered to ship in production.
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AI Solutions We Build
AI Development Services Services We Provide
Custom Model, Fine-Tuned SLM or an API?
Use an API
When a frontier model (GPT, Claude, Gemini) already does the job, we wire it in cleanly with the right guardrails. Fastest and cheapest to start, and often the right answer.
Fine-tune a Small Language Model (SLM)
When you need domain accuracy, data privacy, or lower cost at scale, a fine-tuned SLM frequently beats a general LLM on your specific task, for a fraction of the running cost.
Build or Customize a Model
When the model is your edge, or your data is too sensitive or unique for anything off-the-shelf, we build and fine-tune a custom model that you own and can host yourself.
RAG Over Your Data
When the gap is knowledge, retrieval grounds an existing model in your content, usually the smartest first step before any training.
Not sure whether you need a custom model?
Describe your use case and data, and get a Scope & Design Document (a build-vs-buy recommendation, model/architecture options, and a build-ready plan) in minutes.
Generate My AI Scope
How We Use AI to Accelerate Delivery
Scoping & Documentation
Faster Scope Docs, specs, and technical plans.
Code Generation
Senior engineers direct the work and review every output.
Testing & QA
Broader test coverage, with issues caught earlier.
Research & Integration
Quicker evaluation of tools, libraries, and approaches.
AI Tools vs. Architect-Led AI Development
AI Tools Alone
Generate code without system design or human oversight:
No architecture, so the code has no structural foundation
No security framework or compliance standards
Works for prototypes, but breaks under real production load
Unpredictable timelines, because debugging eats up the speed you gained
DBB's Architect-Led AI Development
AI accelerates the build; the Architect governs the outcome:
A senior architect defines the system before any code is written
Senior engineers review every output before it ships
ISO/IEC 27001-certified practices from day one
Built for production from the start
A clear schedule you can plan around: a working proof of concept in 1 week, a functional MVP in a month
How We Engineer Every Model
Measured Accuracy
Evaluation suites and real accuracy targets from day one, so quality is a number you can track.
Hallucination Control
RAG grounding, output validation, and confidence thresholds keep answers tied to your data, with escalation to a human when confidence is low.
Right-Sized & Model-Agnostic
We use the cheapest model that clears the bar (API, SLM, or custom) behind an abstraction so you're never locked to one vendor.
Your Data, Your Weights
On-prem or air-gapped deployment options; you own the model, the weights, and the data pipeline.
Monitoring & Continuous Improvement
Drift detection, monitoring, and a retraining loop, because a model that isn't maintained quietly degrades.
Responsible AI & Security
Bias checks, explainability, audit trails, and prompt-injection defenses, the same discipline behind the 9-layer security stack on our own AI product.
AI Development Case Studies
Building a Custom AI Chatbot with Deep CMS Integration and MCP Protocol Support
Challenge:
DBB Software needed to convert website traffic into qualified leads, but off-the-shelf chatbots couldn't integrate with its CMS, qualify leads using structured data, or support interoperability with AI agents via MCP.
Solution:
Built a tool-augmented AI chatbot with 16 real-time Storyblok CMS tools.
Implemented the first public MCP server on a company website.
Developed structured BANT lead qualification with GDPR enforcement at the schema level.
Delivered a 9-layer security stack with prompt injection defense and cost controls.
Result:
Production system in 4 weeks with a single developer, 40x cost advantage over SaaS alternatives, and zero-maintenance content sync via real-time CMS integration.
Building an AI Assistant for Junior Tennis Parents
Challenge:
A sports-technology company needed a tournament-planning product for junior tennis, where a parent currently cross-reads federation calendars and eligibility rules by hand.
Solution:
Moved the assistant from a staged plan, retrieve and synthesize workflow to a single agent holding its own tools
Trialled three model families across the rebuild, dropping Xiaomi MiMo and GLM 5.3 Flash before settling on GLM 5.3
Gave that agent three tools over tournament data and a document store, called on demand rather than in a fixed order
Built retrieval over tournament regulations and rule changes using Voyage AI embeddings in pgvector
Confirmed a child's age, level and location against what each event requires, with distance computed from federation coordinates
Tested recommendations against player profiles whose eligible tournaments were known in advance
Result:
A profile built in conversation, fewer parts to tune, and current rules in the answer.
Building a Self-Hosted AI Agent Platform Users Fully Own
Challenge:
A consumer AI studio needed a personal AI platform whose users own the runtime, the memory, and the credentials, which ruled out the vendor-cloud architecture every competing assistant depends on.
Solution:
Built the pod runtime as a single Go binary carrying authentication, an encrypted key vault, a file drive, and audit logging
Devised a supervised agent layer that runs one Python agent process per AI agent and streams its events to the browser
Built persistent memory and agent identity, with a portable skills format and Model Context Protocol tool servers
Created cross-surface packaging and delivery, bundling three runtimes into one desktop installer with continuous deployment
Result:
Data stays local, install is the deployment, and model choice stays open on a platform taken from zero to working in about four months by one senior engineer.
Building a Multi-Agent Runtime With Durable Memory
Challenge:
A consumer AI studio needed several private AI agents running on one install, on top of an agent runtime built for a single person.
Solution:
Bound each running agent to 1 user-and-agent pair, with config, credentials and memory fixed at process start
Enforced permissions in the filesystem mount, a per-profile pre-tool-call guard and per-toolkit allowlists
Built memory as an LLM-maintained wiki with immutable checksummed source and per-viewer citations
Reconciled contradictions in a nightly pass, with human-verified pages holding against agent rewrites
Added scheduled and inbound triggers, failure caps, role-based model routing and a memory benchmark harness
Result:
Shared installs, separate data, enforcement outside the model, and human corrections hold on a platform gated by roughly 7,800 automated tests.
Integrating Front-End Products and Services for an AI Security Platform
Challenge:
A fast-growing AI security company needed its products to work as one, connected to a real-time backend, third-party models, and shared services.
Solution:
Built Red as a real-time red-teaming product on Convex.
Unified the front-end layer across multiple products.
Integrated third-party auth, models, and feature flags.
Automated data sync and shipped a gated public demo.
Result:
The full scan journey works end to end, five apps share one look and feel, and new scan coverage isn't a rebuild, with unfinished work that ships dark and access locked to two checks.
Developing an AI-Powered Design for an E-Commerce Company
Challenge:
An e-commerce company needed help with developing and implementing an AI-powered design assistant.
Solution:
Added AI-powered CTL.
Implemented a 3D rendering engine for room designs.
Developed a virtual designer questionnaire.
Set up a 360-degree iFrame feature.
Result:
30% revenue growth, 12% increase in average order value, 16% boost in conversion rates, and 2x increase in time spent on site achieved.
Improving a Real Estate Platform with an AI-Powered Assistant
Challenge:
A Norwegian proptech company approached DBB Software to add an AI-powered assistant to its real estate platform.
Solution:
Added AI-generated insights widget.
Integrated a WYSIWYG editor for additional customization.
Optimized frontend using Vercel AI SDK for more responsible user interactions.
Result:
Delivered features to enable improved decision-making with an AI-powered assistant and boosted user engagement via dynamic insights and editable content.
Creating an AI-Powered Talent Acquisition Platform
Challenge:
DBBS helped a startup improve its core product, an applicant tracking system, through new features, complete redesign, and scalable architecture.
Solution:
Implemented a modern front end using React, TypeScript, Next.js, Tailwind CSS, and Radix UI for intuitive UX.
Leveraged LLMs and cloud AI services to improve core functionality with AI.
Built out user management, data import/export, CI/CD pipelines, and automated testing.
Optimized performance and reduced AI-related operational costs.
Result:
Successful MVP launch with positive early-adopter feedback, better control over operational costs, and a robust, scalable architecture for future growth.
Advancing an AI-Powered Meeting Management Platform
Challenge:
A technology product company wanted to improve and expand the reach of its AI-powered meeting management platform that records government meetings.
Solution:
Developed an admin panel to manage data and migrate workflows.
Implemented a Boolean search engine for document research.
Created scrapers for pages with live broadcasts in different locations.
Migrated the solution to AWS-based PostgreSQL.
Result:
The solution is moving to a dedicated platform, and the team is implementing a dedicated search engine for audio transcriptions.
Designing a Free AI Reputation Report
Challenge:
Clinicians could not see their own online presence, and the platform had nothing to offer a prospect before a sales conversation started.
Solution:
Reads what AI assistants say about a doctor, not only where they rank
Scores five separate dimensions, then ranks what to fix first
Proceeds on public data alone when the identity match is unclear
Caps what each free report may spend on outside services
Result:
5 scored dimensions, 3 degradation paths with defined behavior, and a spend ceiling on every free report.
Designing an AI Receptionist With Emergency Escalation
Challenge:
A private practice closes at six and the phone keeps ringing. Every unanswered call is a patient handed to whoever picks up first.
Solution:
Specified emergency escalation as a release gate that has to clear a clinician-signed test set
Scoped a bought voice engine and a built platform integration, in that order
Designed a completed call to land as an appointment request in the dashboard staff already use
Specified a route to a human or voicemail behind every failure
Result:
0 dead ends, 1 clinical sign-off gate, 3 defined call outcomes.
AI Bio Writer Grounded in Confirmed Facts
Challenge:
Most clinicians leave their profile bio blank, and the writing was landing on the platform's own team instead.
Solution:
Specified grounding in facts the clinician confirms first, with a missing field halting generation
Specified sanitization of open-web research before it reaches the model
Removed every path that publishes without the clinician doing it
Specified what happens when the research, the model or the flow itself fails
Result:
0 automatic publishing paths, 4-question intake, 1 adoption bar.
Designing an MCP Server for AI Assistant Discovery
Challenge:
A global healthcare review platform needed AI assistants to query its verified specialist directory, without handing over the review corpus.
Solution:
Specified a stateless read service on the platform's existing read layer, with no new datastore
Specified nine read-only tools as a contract, with no language model inside the server
Fixed the data-exposure line in code, enforced by property-tested response mappers
Specified an internal-first rollout behind a named security gate with five signed exit criteria
Result:
One AI layer for every product, layered security before public exposure, the review corpus as an owned asset.
Designing a Self-Hosted Patient Chatbot With Server-Enforced Safety
Challenge:
A healthcare platform had a third-party chat window on its homepage that patients were typing health information into, with no emergency handling.
Solution:
Specified a deterministic emergency gate over the rolling conversation window, ahead of any model call
Specified server-side validation of every answer, inside a two-second first-token budget
Reused the shared tool layer and the enquiry and booking flows already in production
Wrote down every store a conversation touches, so an erasure request completes
Result:
Guardrails owned by the client, patient health data held in-house, a calculable worst-case bill.
Designing a Hallucination-Resistant Search Mode for a Healthcare Platform
Challenge:
A healthcare marketplace wanted patients to search in their own words, using the search and infrastructure it already ran.
Solution:
Specified one model call, isolated from the patient-facing application, over the unchanged search path
Constrained the model to a closed schema, with the fields it must not set never shown to it
Gated translation accuracy in continuous integration against a versioned labeled set
Specified an experiment whose decision rule, guardrails and kill switch were written first
Result:
A search upgrade without new infrastructure, health data at the Article 9 standard, a decision, not a feature.
Testimonials
“DBB Software's commitment to delivering outstanding AI and custom software solutions was truly impressive”
Mariam Asatryan
COO, Software Engineering Company
“DBB Software delivered the first version in record time. The team continues to work on the website with a sense of ownership and pride in their work.”
Alex Shyba
CTO, Uniform
"Impressive what they have managed to make in such a small time, also suggesting new ways to implement, or new technologies we should be aware of.”
Peder Søholt
CTO & Co-Founder, Plaace
"They are skilled, communicative, and dedicated workers. DBB Software has delivered the project on time and with high quality, exceeding the client's expectations"
Alon Gilady
CEO, Renovai
"Their level of engagement and collaboration on each project are impressive. The engagement has reduced call center costs and increased overall consumer growth"
Name withheld under NDA
Product Manager, DispatchHealth
Our Certifications
DBB Software, a certified partner for AWS, Microsoft Azure, and MongoDB, delivers secure, scalable projects.
Long-Term Partnerships & Support
With 80% of clients staying 7+ years and our team collaborating for over 5 years, we ensure dedicated long-term support.
Quality Assurance & Standards Compliance
Adherence to CMMI and ISO standards for continuous improvement, quality, and process optimization.
Agile and SCRUM Methodologies
Flexible, transparent, and collaborative project management using Agile and SCRUM methods.
Scope Your AI Project in Seconds
Detail your AI use case and data to get a free, instant scope: a build-vs-buy recommendation, model options, and effort tiers, right now.
Generate AI Scope
How We Deliver
From the first conversation to production, DBB combines senior AI engineers, specialized AI agents, and milestone-based delivery to keep scope, progress, and ownership clear.
Understand the Outcome
We start with your business goal, users, your data, technical environment, constraints, and the accuracy bar you actually need. We listen first and recommend the right path instead of pushing a predefined package.
Define the Right Engagement
Depending on your needs, we propose a build-versus-buy assessment before anything is trained, a fixed-scope model or LLM application build, or ongoing accuracy engineering and MLOps after deployment. You receive a clear delivery plan with scope, milestones, responsibilities, timeline, and budget.
Design the Solution
Our senior engineers define the architecture, data pipelines, integrations, infrastructure, security, and deployment approach. Where AI is involved, we also define model strategy, retrieval design, evaluation, human oversight, and fallback logic.
Build in Milestones
We deliver working functionality in short, visible milestones, with accuracy measured at each one rather than claimed at the end. Our engineers use specialized AI agents across planning, development, testing, and documentation, while remaining accountable for architecture, code quality, security, and final decisions.
Validate, Launch, and Evolve
We test accuracy against an evaluation suite, plus integrations, performance, cost, and security, before release. After launch, we support deployment, handover, monitoring, drift detection, and the retraining loop that keeps the model useful.
Have a model, an LLM application, or an AI feature to build?
Tell us what you are trying to achieve. We will help you determine the right technical and delivery approach.
Mina Morkos
Business Development Manager
Discuss Your Project
Transforming Multiple Industries With Tech Expertise
E-Commerce
Real Estate
Transformation & Logistics
Travel & Hospitality
EdTech
HR Platforms
Social Networks
Healthcare & Biotech
FinTech
AI Development Tech Stack
Each project gets a tailored stack for timely delivery and clean code. Drawn from the technologies our engineers work with day to day:
Google Cloud
Scikit Learn
NLTK
YOLO
GPT-3
PyTorch
MS Azure
AWS
SpaCy
Keras
Tensor Flow
OpenCV
FAQ
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"Our 10 years of software development expertise are embedded in our architect-led, AI-accelerated delivery, so you don't start from scratch; we set everything up fast and build to production standards.
Interested? Fill out the form and book a free consultation!"
Mina Morkos
Business Development Manager
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