// ai_solutions
AI Solutions
We embed AI into your product - from RAG pipelines and workflow automation to domain-specific chatbots with CRM and helpdesk handoff - with reliability, cost controls, and UX that holds up in production.
// what we build
What you can hire us to do
We build production AI: models, retrieval, agents, and vision systems wired into your product — with evaluation, cost control, and a human handoff when the model shouldn't be in charge. Hover a capability on desktop, or tap it on a phone, to see what that engagement includes.
01
Generative AI & NLP
Design and development of LLM-powered applications, RAG pipelines, agents, and conversational systems — using OpenAI, LangChain, FAISS, Pinecone, Hugging Face, and modern LLM frameworks.
- LLM-powered applications
- Product features that call large language models for real work — drafting, routing, extraction — not a ChatGPT iframe on your site.
- RAG pipelines
- Retrieval-Augmented Generation over your documents and systems, with chunking, embeddings, and citations instead of stuffed prompts.
- AI agents
- Tool-using agents that take actions in your stack (search, tickets, CRM) with guardrails and an audit trail.
- Intelligent chatbots
- Domain chatbots grounded in your knowledge base, with intents, fallback, and the ability to admit when they don't know.
- Workflow automation
- LLM steps inside existing processes: classify, extract, draft, route — with a human approval gate where it belongs.
- Conversational AI
- Multi-turn experiences across web, Slack, or helpdesk that keep context and hand off cleanly.
- Prompt engineering
- Prompts, tools, and eval sets treated as product code: versioned, tested, and cheap enough to run.
- Semantic search
- Embedding search so people find the right policy, SKU, or paragraph even when they don't know the keyword.
- Question answering
- Grounded Q&A over your corpus with sources shown in the UI — not confident fiction.
- Text generation
- On-brand drafts for support, sales, or content, with review workflows so the model doesn't publish unsupervised.
- Summarization
- Thread, document, and meeting summaries that preserve decisions and action items.
- Embedding-based retrieval
- Vector indexes, hybrid search, and reranking tuned for your corpus — not a default chunk size from a tutorial.
- Document intelligence
- Parse, classify, and extract from PDFs, contracts, and scans into structured data your systems can use.
- Vector databases
- Pinecone, FAISS, pgvector, and the ingestion jobs that keep them in sync with source-of-truth systems.
- AI-powered assistants
- In-product copilots for your users or internal team, scoped to tools they are allowed to use.
- Enterprise AI solutions
- SSO, tenancy, logging, and vendor choices that match how your security team actually signs off.
02
Computer Vision
End-to-end vision systems: classification, detection, tracking, OCR, and live video — trained, optimized, and deployed with YOLO, RT-DETR, CNNs, OpenCV, and OpenVINO.
- Image classification
- Custom classifiers for your categories — QA, moderation, sorting — trained on your data and evaluated on the cases that actually fail.
- Object detection
- Locate and label objects in images or frames for inventory, safety, or ops workflows.
- Custom object detection
- Detectors for the things generic models have never seen: your parts, your defects, your uniforms.
- Image segmentation
- Pixel-level masks for measurement, medical, or manufacturing tasks where a bounding box isn't enough.
- Object tracking
- Keep identity across frames for queues, traffic, or sports — not a fresh detection every tick.
- Face detection & recognition
- Detection and optional recognition with the privacy and consent model your use case requires.
- Custom recognition systems
- Identify logos, SKUs, documents, or other visual identities unique to your operation.
- OCR
- Text from photos, scans, and IDs — including messy lighting — into fields your workflow can validate.
- Real-time video analytics
- Live insights from cameras: counts, events, alerts, with a pipeline that doesn't melt the GPU on hour two.
- RTSP stream processing
- Ingest and process camera streams for on-prem or edge deployments, not only uploaded files.
- Medical image analysis
- Assisted analysis workflows with the validation, audit, and 'model is not a clinician' UX the domain demands.
- Model optimization
- Quantization, pruning, and architecture choices so the model fits the hardware you actually have.
- Inference acceleration
- Batching, TensorRT/OpenVINO-class runtimes, and latency budgets for interactive or live systems.
- Edge AI deployment
- Models on device or on-prem when the video can't leave the building — plus a story for updates.
- Production vision apps
- The product around the model: UI, alerts, human review, and monitoring so a demo becomes a system.
03
Machine Learning
End-to-end ML pipelines: from messy data and EDA through training, evaluation, and a deployed model your team can retrain.
- Supervised learning
- Classification and regression models trained on labeled data you have — or a labeling plan for the data you don't.
- Unsupervised learning
- Clustering and structure discovery when labels don't exist yet but the data still has a shape.
- Classification
- Risk, intent, fraud, quality — models with precision/recall tradeoffs you choose, not a default 0.5 threshold.
- Regression
- Forecasts and scores with error bars and a clear story for when the model is out of distribution.
- Clustering
- Segments and anomaly groups that product and ops can actually name and act on.
- Ensemble learning
- Boosting, bagging, and stacked models when a single learner isn't honest enough.
- Feature engineering
- The unglamorous work that usually wins: leakage checks, encodings, and features tied to how the business actually works.
- Exploratory data analysis
- A first pass that surfaces data quality, bias, and whether ML is even the right tool.
- Data preprocessing
- Cleaning, joins, and pipelines that run the same in training and production.
- Model evaluation
- Holdouts, slices, and business metrics — not only accuracy on a lucky test set.
- Hyperparameter tuning
- Search with a budget, so we don't burn a week of GPU for a 0.2% vanity lift.
- Model deployment
- APIs, batch jobs, or in-app scores with versioning and a rollback when the new model is worse.
- MLOps fundamentals
- Training/serving parity, feature stores-lite, and the alerts that catch silent model drift.
- Production ML solutions
- The full path: data → model → endpoint → monitoring, owned by one team.
04
Deep Learning
Design, training, fine-tuning, and deployment of neural networks with TensorFlow and PyTorch — including the optimization that makes them affordable to run.
- Artificial neural networks
- Tabular and mixed-input networks when gradient boosting isn't the whole story.
- Convolutional networks
- CNN architectures for vision and spatial data, including transfer from strong backbones.
- Recurrent networks
- Sequence models for time-ordered data when a transformer is overkill or the data is short and regular.
- LSTM networks
- LSTM/GRU models for longer temporal dependencies in sensors, logs, or language-adjacent tasks.
- Transfer learning
- Fine-tune strong pretrained models on your (usually small) dataset instead of training from scratch.
- Fine-tuning
- Task-specific heads and training recipes with regularization so you don't memorize 800 examples.
- Model optimization
- Distillation, quantization, and architecture search with a latency/cost target.
- Inference acceleration
- Runtime and hardware choices so a research checkpoint becomes an interactive feature.
- TensorFlow & PyTorch
- Training and export in the framework that matches your team, with ONNX/runtime bridging when serving needs it.
- Production deployment
- Packaged models, GPU/CPU serving, and the eval harness that gates every new checkpoint.
05
AI in production
The work that turns a notebook into a product: evaluation, cost, safety, CRM/helpdesk wiring, and a human in the loop.
- Evaluation harnesses
- Golden sets and online evals so we know if a prompt or model change actually got better.
- Cost & latency control
- Caching, smaller models, retrieval instead of huge context, and budgets with alerts.
- Guardrails & safety
- PII handling, topic limits, tool allowlists, and refusal behavior that matches your risk.
- Human handoff
- Escalation into helpdesk or Slack with the full transcript so agents don't start from zero.
- CRM & helpdesk integration
- HubSpot, Intercom, and friends — tickets, contacts, and bot transcripts in the tools your team already lives in.
- AI observability
- Traces, token usage, and quality dashboards — because 'the model felt worse this week' is not an ops strategy.
- Gated rollouts
- Ship behind flags, to one workspace, with a kill switch when retrieval goes sideways.
- Private data design
- Least-privilege retrieval, redaction, and provider choices that match how you are allowed to use customer data.
// ideal for
- You have a clear workflow or conversation AI should handle - not a vague 'add ChatGPT' request
- Reliability, cost, and UX matter as much as model novelty
- You want production integration with handoff and monitoring, not a disposable demo
- →LLM API integration (OpenAI, Anthropic, etc.)
- →RAG & vector search pipelines
- →Custom knowledge base training
- →Workflow automation
- →Conversational agents & chatbots
- →CRM & helpdesk integration
- →Human handoff workflows
- →AI-powered analytics
- ✓Production AI feature or chatbot
- ✓Prompt engineering & evaluation
- ✓Knowledge base setup
- ✓Cost & latency optimization
- ✓Monitoring & fallback strategy
// how we deliver
Our AI Solutions approach
A service-specific path - still rooted in Discover → Architect → Build → Ship.
Pin the use case
Define success metrics, data sources, intents, handoff rules, and where a human must stay in the loop.
Prototype & evaluate
Rapid prompt/RAG experiments with an evaluation set - and knowledge base ingestion where chat is in scope.
Integrate & deploy
Wire models into your product or channels with auth, rate limits, CRM sync, and graceful degradation.
Operate & optimize
Monitor cost, latency, and quality. Tune retrieval, prompts, and conversation flows as real traffic arrives.
// proof
Work involving AI Solutions
Selected engagements where this discipline was part of the delivery.
// from a client
“We were bleeding leads because our we didn't have a website that actually showed what the AI does. MintyLogix fixed that for us and built a site that actually shows what the AI does — the call flow, the CRM handoff, the industry pages — instead of vague buzzwords. Prospects show up to fit calls already understanding the product, and the pricing section answers half the questions before we even talk.”
Umair Zafar
Founder, FrontDesk AI · After-hours leads captured
// stack
Tools we reach for
// engagement
What working together looks like
Soft guidance - final scope and pricing live on our pricing page.
Typical timeline
2–8 weeks for a production feature or pilot
Team shape
AI engineer + product/integration engineer
Starting point
Small project or retainer — start with a fit call; see Pricing
Related services
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