Custom AI Development

Large language model systems designed around your specific data, domain vocabulary, and use case, not a wrapper around a generic chatbot that happens to be connected to your documents. RAG (retrieval-augmented generation) pipeline architecture: documents ingested via PDF parsing (pdfminer, PyMuPDF), HTML scraping, or API export; chunked with overlap-preserving sliding window strategy (512-token chunks, 50-token overlap for semantic continuity); embedded using OpenAI text-embedding-3-large, Cohere embed-v3, or open-source sentence-transformers/all-mpnet-base-v2 depending on your privacy and cost requirements; stored in a vector database (Pinecone, Weaviate, pgvector on PostgreSQL, or Chroma for lower-volume on-premises deployments). Retrieval: hybrid search combining dense vector similarity (cosine distance) with sparse BM25 keyword matching (via Elasticsearch or OpenSearch) for queries that include exact product codes, part numbers, or domain-specific terminology that embedding similarity handles poorly; top-k results reranked with Cohere Rerank or cross-encoder models before being passed to the LLM. LLM selection: GPT-4o for highest accuracy on complex reasoning tasks; Claude 3 Opus/Sonnet for document-heavy retrieval and long-context understanding; Llama 3 or Mistral deployed on private infrastructure (Azure OpenAI, AWS Bedrock, or self-hosted on GPU instances) for use cases with data residency or confidentiality requirements. Prompt architecture: system prompt encodes domain context, response format, and behavioural guardrails; few-shot examples for output structure; chain-of-thought prompting for multi-step reasoning tasks. Hallucination controls: LLM instructed to cite source chunks; confidence grading applied; retrieved context window limited to verified sources. Deployed as a REST API or embedded in your product UI.

Multi-step AI agents that plan, retrieve information, call tools, and take actions in your systems, completing workflows that currently require a human to coordinate steps across multiple systems. Agent architecture using the ReAct (Reason + Act) pattern: the LLM reasons about the next step, calls a tool or API, receives the result, incorporates it into context, and continues until the goal is achieved or it determines it cannot proceed without human escalation. Tool definitions: structured tool schemas provided to the LLM (OpenAI function calling, Anthropic Claude tool use, LangChain tool interface) define what the agent can do, query your database, call an external API, read a file, write to a CRM, trigger a downstream workflow. Agent frameworks used based on complexity: LangChain/LangGraph for graph-based workflow agents with explicit state management; AutoGen for multi-agent conversations where specialist agents collaborate (a data retrieval agent hands off to a reasoning agent hands off to an action agent); CrewAI for task-decomposition workflows with role-defined agents; custom thin agent loops for simple linear workflows where framework overhead isn't warranted. Examples built: insurance claims triage agents that read the claim document, query the policy database, check the customer history, apply the coverage rules, and produce a recommended settlement amount with the reasoning chain for human review; data enrichment agents that take a list of company names, call LinkedIn, Clearbit, and Companies House APIs, deduplicate and reconcile conflicting data, and write enriched records back to the CRM; research assistants that search the web, read full-page content, cross-reference multiple sources, and produce a structured briefing document. Human-in-the-loop: configurable escalation points where the agent pauses and presents its reasoning for human approval before taking a high-stakes action, the agent proposes, a human approves, the agent executes.

Computer vision systems trained on your specific images and your specific defect types, document layouts, or object categories, not a general-purpose model that performs adequately on generic benchmark images but misses the specific defects your quality team knows on sight. Model architecture selection: YOLOv8/YOLOv9 (Ultralytics) for real-time object detection where inference speed matters (production line inspection at 30+ fps, retail shelf scanning); EfficientNet-B4/B7 for classification tasks where accuracy matters more than speed (pharmaceutical label verification, regulatory document classification); Detectron2 (Mask R-CNN) for instance segmentation where shape and boundary precision is required (PCB trace defect mapping, wound area measurement in healthcare imaging); custom CNN architectures for specialised tasks where pre-trained backbones don't fit the input modality. Transfer learning strategy: start with pre-trained weights (ImageNet, COCO, or domain-specific pre-training if available) and fine-tune the final layers on your labelled images; reduces the labelled data requirement from hundreds of thousands to typically 500–5,000 images per class depending on visual complexity. Data labelling: Roboflow, CVAT, or Label Studio used for annotation workflow; annotation protocol written for your specific defect taxonomy before labelling begins to ensure consistency across labellers; inter-annotator agreement measured and poor-quality labels filtered before training. Training infrastructure: PyTorch on AWS p3/p4 GPU instances or Google Cloud A100 instances; Weights and Biases (wandb) for experiment tracking; trained model weights exported to ONNX or TensorRT for inference optimisation before production deployment. Deployment: REST API endpoint (FastAPI) serving predictions; model containerised in Docker; GPU inference with batch processing for throughput-sensitive applications; edge deployment on NVIDIA Jetson for on-premises real-time inspection without cloud latency.

Machine learning models trained on your specific historical data to produce predictions that drive business decisions, not models tuned to perform well on generic benchmark datasets that share no resemblance with your actual data distribution. Model selection by task type: LightGBM and XGBoost for tabular prediction tasks (churn prediction, credit risk scoring, demand forecasting, fraud detection), tree-based ensemble methods consistently outperform neural networks on structured tabular data and train orders of magnitude faster; LSTM and Temporal Fusion Transformer (TFT) for time-series forecasting where sequential patterns and multi-horizon predictions matter (inventory demand forecasting, energy consumption prediction, subscription retention forecasting); Isolation Forest and Autoencoder-based models for anomaly detection in high-dimensional data where supervised labels for anomalies are rare; Random Forest and logistic regression as baseline comparators before investing in complex model architectures. Feature engineering is 70% of the work: lag features, rolling statistics (7-day, 30-day, 90-day mean and std), calendar features (day of week, month, holiday flags), interaction terms, and domain-specific derived features built in collaboration with your subject matter experts who know which signals are causally relevant. Evaluation metric selection tied to the business decision: MAPE/WAPE for demand forecasting; AUC-ROC for ranking (churn risk scoring); precision-recall for imbalanced classification (fraud, rare defect detection); MAE/RMSE only when the error distribution is symmetric and outliers are not disproportionately costly. MLflow for experiment tracking, model versioning, and model registry; feature store (Feast or a custom Redis-backed store) for consistent feature computation between training and inference; Evidently AI or custom statistical tests for data drift detection post-deployment. Model inference API deployed as a microservice with p50/p95/p99 latency monitored and served within your SLA.

Natural language processing systems fine-tuned on your specific document types and domain vocabulary, because a general-purpose NER model trained on Wikipedia and news articles has never seen your contract clause structures, your clinical terminology, your financial instrument names, or your regulatory form fields. Named entity recognition for domain-specific entities: SpaCy custom NER pipeline or Hugging Face token classification model (BERT, RoBERTa, or domain-specific variants like BioBERT for biomedical, LegalBERT for legal, FinBERT for financial text) fine-tuned on your annotated examples; entity types defined in collaboration with your domain experts (contract parties, payment terms, regulatory dates, product codes, facility names, whatever your documents contain that needs to be extracted reliably). Document classification: multi-class or multi-label classification using fine-tuned sentence-transformers or DistilBERT for routing incoming documents to processing queues (contract type classification, support ticket category, regulatory submission type); zero-shot classification via Hugging Face NLI models for new document categories that don't yet have enough labelled examples for supervised training. Relationship extraction: identifying relationships between entities in the same document (which product specification clause applies to which component, which payment term applies to which contract party) using SpaCy's dependency parsing or custom span classification models. Sentiment and opinion analysis calibrated to your domain: general-purpose sentiment models score "delayed" as neutral in most contexts, fine-tuning on your customer feedback, support tickets, or survey responses calibrates the model to your specific vocabulary and domain norms. Text summarisation for document processing: abstractive summarisation using fine-tuned T5 or BART models that produce domain-appropriate summaries respecting your document structure, rather than generic extractive summarisation that just copies the first sentences. Inference deployment as a REST API with batch processing support for bulk document ingestion.

AI systems in production degrade silently unless you have measurement infrastructure in place, the model that performed at 94% accuracy at launch performs at 81% twelve months later because the real-world data it encounters has drifted from the distribution it was trained on, and no one noticed because there was no monitoring. Offline evaluation framework built before the first model iteration: a held-out test set drawn from your actual data (not a public benchmark), evaluation metrics chosen for your business decision context (precision, recall, F1 per class for classification; WAPE, MASE for forecasting; BLEU, ROUGE, LLM-as-judge for text generation quality), and a pass/fail threshold defined so model performance is measured against a standard, not compared to an arbitrary previous version. Prediction logging: every prediction made in production logged with the input features, the model version, the prediction output, and a timestamp; logs stored in a queryable store (BigQuery, Redshift, or PostgreSQL) so any time a model output causes a downstream business error, the prediction can be reconstructed and analysed. Ground truth collection pipeline: in workflows where outcomes are observable (the churn prediction made on May 1, did the customer actually churn by May 31?), the ground truth is automatically collected and joined to the prediction log; allows ongoing accuracy measurement without manual labelling effort. Data drift detection using Evidently AI or custom statistical tests (Population Stability Index for categorical features, KS-test for continuous distributions) run on weekly or monthly batches of incoming data compared to the training distribution; alert generated when PSI exceeds 0.2 (the threshold typically indicating meaningful distribution shift requiring model review). Model retraining cadence: monthly automated retraining pipeline triggered if drift metrics exceed thresholds or if ground-truth accuracy drops below the defined minimum; new model version evaluated against the evaluation framework before being promoted to production, with automatic rollback if the new version underperforms the current production model.

Before any architecture decisions, cost estimates, or technical proposals, we assess whether your data can support the AI system you need, because the biggest waste in custom AI projects is building the system before confirming the data can sustain it. Data quality assessment: we sample 200-500 records from your actual data and score them on completeness (missing values per column and their impact on the target prediction), consistency (value ranges, format consistency, duplicate records), label quality (for supervised learning: agreement rate between labelled examples, evidence of labelling inconsistency or bias), and recency (is historical data from three years ago representative of today's patterns). Quantity assessment: for supervised classification, we estimate whether your labelled dataset is sufficient for the performance target (a rough heuristic: 1,000+ examples per class for tabular classification, 500–5,000 labelled images per class for computer vision, 1,000+ annotated documents for NLP fine-tuning, but these vary significantly by task complexity). For RAG-based LLM systems, data quantity matters less; knowledge coverage matters more: we map the question types users will ask against your document library to identify coverage gaps. Feasibility verdict with specifics: not a generic "this should work" but a written assessment of what accuracy is achievable, what the limiting factors are, what data collection or labelling would improve it, and what the risks are of the current dataset. If the data isn't sufficient, we design the minimum viable data collection programme, annotation sprint, active learning loop, synthetic augmentation, or structured data collection from your operational system, as a pre-phase before model development.

Defining success before writing a line of model code prevents the most common AI project failure: a model that performs "well" by some metric that was chosen for convenience rather than relevance to the business decision the system is meant to support. Evaluation framework definition covers four things: the evaluation metric, the acceptable threshold, the test dataset, and the business decision the metric is proxying for. Evaluation metric selection is a business decision, not a technical default: accuracy is the wrong metric for imbalanced classes (a fraud detection model that labels everything as not-fraud achieves 99.7% accuracy on typical fraud rates); precision and recall trade-offs are made based on your cost of false positives vs false negatives (a medical screening system tolerates high false positives to avoid missing true positives; a content moderation system at scale may prioritise precision to avoid over-blocking); WAPE (weighted absolute percentage error) is preferred over MAPE for demand forecasting because MAPE penalises under-forecasting asymmetrically and produces infinity for zero-demand periods. Test dataset composition: held-out set drawn from recent data (not a random split of all historical data, which overstates performance on future data when data has temporal drift); edge cases explicitly included (the difficult invoice layouts, the ambiguous defect types, the unusual transaction patterns) that represent the failure modes your domain experts can identify. Performance threshold set as a minimum below which the system is not deployed: a 70% accurate churn model may make worse decisions than a trained human analyst; the threshold is set at the point where the model provides positive expected value over the current manual process. These definitions are written down, reviewed with your team, and form the acceptance criteria for the AI system delivery.

AI model development follows an empirical loop that typical software development doesn't: train, evaluate, identify failure modes, decide whether to improve the data or the model, change one thing, retrain, compare. Each iteration is measured against the agreed evaluation framework, not against the previous iteration's score, so progress is measured against the defined target, not relative to wherever you started. First iteration baseline: train the simplest model that could plausibly work on your data (logistic regression, fine-tuned DistilBERT, a YOLOv8s model) to establish a baseline and identify the dominant error types before investing in architectural complexity; this prevents spending three weeks optimising a large model only to discover that a simpler model achieves comparable performance at 10% of the inference cost. Error analysis per iteration: confusion matrix analysis for classification tasks to identify which class pairs are being confused (and whether the confusion is due to label ambiguity, insufficient training examples, or genuinely hard cases); qualitative review of the 50 highest-error predictions to understand failure patterns that aggregate metrics don't reveal. Data improvement vs model improvement decision: most AI performance improvements come from better training data, not from model architecture changes; analysis identifies whether the model is failing because it hasn't seen enough examples of a pattern (data gap, more labelled examples), because the labels are inconsistent for a category (label quality issue, re-annotation), or because the pattern genuinely exceeds what the model architecture can represent (architecture change warranted). Hyperparameter optimisation using Optuna or Ray Tune for automated search over the model configuration space after the data and architecture are settled. MLflow experiment tracking maintains the full history of iterations so any previous model version can be reproduced and re-evaluated against new test data.

A model that achieves 93% accuracy on the evaluation framework but takes 8 seconds to return a prediction and requires a data scientist to operate it is not a production AI system, it is a prototype. Production integration means the model is exposed as a performant API, integrated into the workflow where decisions are made, and operable by your team without specialist intervention. API layer: FastAPI or Flask REST API wrapping the model inference function; input validation with Pydantic schemas to prevent malformed requests reaching the model; structured JSON response including the prediction, confidence score, and (where applicable) the supporting evidence or feature contribution; p99 inference latency target defined during scoping and enforced by load testing with k6 or Locust before go-live. Model serving infrastructure: Dockerised model container with versioned image tags; deployed on AWS ECS Fargate, GCP Cloud Run, or Kubernetes based on your infrastructure preference; GPU inference instances for vision and large LLM models, CPU inference for tabular models and smaller NLP models where GPU adds cost without meaningful latency benefit; auto-scaling based on request queue depth. Data pipeline integration: inference inputs pulled from your existing systems (database query, webhook payload, message queue consumer) rather than requiring your team to export data to a separate tool; prediction outputs written back to the originating system (database update, CRM field update, downstream workflow trigger). Monitoring: Prometheus metrics for request count, latency distribution, error rate, and prediction distribution (for detecting silent model degradation when input distributions shift); Grafana dashboard surfacing these metrics; PagerDuty alert on elevated error rate or latency exceeding the defined SLA. Full handover: Docker image, inference code, deployment configuration, monitoring dashboards, and operational runbook provided at project completion.