Production AI agents, RAG systems and LLM workflows built with evals, governance and observability — designed to ship to real users, not impress a deck.
01 / A better starting point
Most businesses lose customers every day to underperforming custom AI development. If any of these sound familiar, you are losing revenue right now:
02 / Built around your ambition
We build task-specific agents and copilots that call your tools and data through defined functions, with permission checks and clear hand-offs back to staff.
Your documents are chunked, embedded and retrieved with source citations, so model answers stay grounded in current company knowledge rather than general training data.
Where retrieval alone is not enough, we tune prompts or fine-tune models on your examples to improve format, tone and task accuracy against measured baselines.
We assemble gold-standard question and answer sets with scoring rubrics, then run them in CI so every prompt or model change is tested before release.
Dashboards track latency, spend per request, refusal rates and failure patterns, giving your team early warning when behaviour drifts or costs start climbing.
We select and configure Pinecone, Weaviate or pgvector based on data volume, filtering needs and hosting constraints, including indexing and re-embedding pipelines.
Models from OpenAI, Anthropic, AWS Bedrock or Google Vertex are wired in behind an abstraction layer, so you can switch providers per task without rewriting features.
For strict data residency, we deploy open models such as Llama or Mistral on your Kubernetes cluster, including GPU scheduling, scaling and inference monitoring.
The Payoff
Retrieval over your own policies, products and past work means answers reflect your business rather than the open internet.
A provider-neutral architecture lets you move between LLM vendors or open models without rebuilding the product.
Input handling, permission boundaries and output checks reduce the risk of users manipulating the system.
Caching, model routing and usage dashboards keep cost per task visible and under control.
How we work / From idea to impact
We map the task, the users and the data available, and flag early whether AI is the right tool for it.
Before writing prompts we collect representative inputs and expected outputs, so every later change can be tested against them.
We choose between retrieval, fine-tuning, agents or simple prompting based on the evaluation results, not preference.
The application is built with access controls, input filtering, fallbacks and clear escalation to a person when confidence is low.
We release to a small internal group first, review logs and feedback, then widen access once behaviour is stable.
We keep evaluations running as models and data change, and handle updates so quality does not quietly drift.
Why Devintek
Every custom AI build ships with its own evaluation suite.
We design so you are never locked to a single LLM provider.
Security reviews cover prompt injection and data leakage, not just hosting.
Honest advice when a rules-based approach would do the job better.
Full handover of code, prompts and documentation to your team.
Direct access to the developers building your system.
Your vision. Our craft.
Strategy, design and development working as one connected team.
Industries
Investment
From $15K
From $60K
Custom quote
FAQ
Our team replies within one business day with a straight answer, not a sales pitch.
Contact usCustom AI solution development services have become one of the highest-leverage investments a business can make in 2026 — when the AI is built with engineering discipline. Devintek builds custom AI for businesses that need production-grade systems, not demos. Every custom AI engagement starts with discovery: identifying the specific workflow where AI has the highest ROI, scoring it on frequency, manual cost, error rate and data availability. We then design the system architecture — choice of base model (GPT-4o, Claude 3.5, Gemini 1.5, or self-hosted Llama/Mistral/Qwen), retrieval strategy (RAG with vector databases like Pinecone, Weaviate or pgvector), prompt engineering with structured outputs, guardrails for prompt injection and PII, and the eval suite that will score every output against a labelled gold set. We deploy with full observability: latency, cost, refusal rates, hallucination detection, user feedback collection, and rate limiting. Versioned prompts and model deployments mean behaviour is reproducible. Custom AI projects we have shipped include customer support copilots over ticket history, internal knowledge RAG over engineering docs, sales call summarisation with CRM auto-population, contract review with deviation flagging, ecommerce catalogue enrichment at SKU scale, and decision-support agents for finance and operations teams. Each ships with documented architecture, automated evals running in CI, monitoring dashboards, and a rollback plan so the AI can be turned off if something goes wrong without breaking the business.
Let's discuss your goals on a free 30-minute strategy call. You will leave with a written roadmap — whether you hire us or not.
Strategy / Design / Technology
