AI & Machine Learning
Production AI systems and ML — not demos.
There is a lot of noise in AI right now. Our job is to tell the difference between what is worth building and what is a demo that falls over in week two — and to ship the kind that holds up. Production systems, not demos.
We build practical machine learning: a model that predicts the thing your business runs on, an assistant that answers from your own documents, a pipeline that quietly classifies and routes work your team used to do by hand. Every model ships with an honest evaluation, so you know what it is good at before it touches a customer.
And because a model in a notebook is not worth much, we deploy it, wrap it in an API or UI, instrument it for drift and cost, and keep an eye on how it behaves once real data hits it. The AI work is part of the product — not bolted on afterwards.
- Hours of manual work handled automatically and reliably
- Decisions and predictions backed by your own data, not generic benchmarks
- An AI feature that survives contact with real users and real edge cases
Capabilities
Predictive & ML models
Forecasting, churn and demand prediction, lead scoring, classification and regression — trained, validated and evaluated on your data, not a benchmark dataset.
LLM & RAG applications
Chatbots and assistants, retrieval-augmented generation over your own documents, summarisation, extraction and drafting tools — grounded, hallucination-aware and observable.
AI agents & orchestration
Multi-step autonomous agents that plan, use tools and hand off to humans at the right moments — built on reliable orchestration frameworks, not fragile prompt chains.
NLP & document AI
Sentiment, entity and intent extraction, semantic search, and turning piles of unstructured text or PDFs into structured, queryable data.
Computer vision
Image classification, object detection and OCR for inspection, tagging and document workflows where the input is pictures, not rows.
Recommendations & personalisation
Ranking, related-item suggestions and tailored experiences driven by observed behaviour rather than editorial guesswork.
Fine-tuning & evaluation
Adapting foundation models to your domain and building the eval harness that proves they are measurably better — before you ship, not after.
Deliverables
- A trained model or AI feature, deployed and callable via API or embedded in your product
- An honest evaluation report: accuracy, limits and known failure modes
- Observability setup — latency, cost and drift monitoring in production
- Running-cost notes and a clear model for ongoing inference spend
Best suited for
- Founders adding a genuine AI feature — not a wrapper — to a product
- Support and operations teams who want an assistant grounded in their own documents
- Teams replacing tedious manual review or classification with a reliable model
- Anyone sitting on data or documents they wish they could query intelligently
What we build with
Applied ML, LLMs & MLOps
Applied machine learning is the discipline of taking statistical learning algorithms out of research papers and into production systems that make decisions at scale. Classical ML — gradient-boosted trees, logistic regression, recommendation engines, anomaly detectors — still solves the majority of real business problems faster, cheaper, and more interpretably than any neural approach. Knowing when a well-engineered XGBoost pipeline outperforms a transformer is the first thing a competent ML team learns.
Generative AI and large language model (LLM) systems are a distinct engineering problem layered on top. An LLM API call is not an AI product. Production LLM systems require prompt design backed by an eval harness, retrieval-augmented generation (RAG) to ground the model in proprietary data and cut hallucinations, guardrails for safety and brand compliance, latency-aware serving, and cost instrumentation — the same rigor you would apply to any critical microservice.
MLOps is the set of practices that keeps both kinds of models honest after launch. It spans experiment tracking, reproducible training pipelines, model versioning, A/B testing in production, feature stores, data and concept drift detection, and the feedback loops that let models improve instead of silently decay. A model that nobody monitors is a liability, not an asset. Production AI means measurable business outcomes, observable system behaviour, and a documented path to retraining when the world changes.
ML Project Lifecycle
Most ML projects fail not in the modelling phase but at the boundaries — unclear problem framing at the start, and missing observability at the end. This lifecycle enforces checkpoints at those boundaries.
- Translate the business objective into a precisely scoped ML task — classification, ranking, regression, retrieval, or generation
- Audit available data for volume, freshness, label quality, and coverage of the prediction distribution
- Establish a baseline: rule-based heuristic or human performance sets the minimum bar the model must beat
- Define offline metrics (precision/recall, MRR/NDCG, BLEU/ROUGE) and the business KPI they proxy
- Document data lineage, PII exposure, and regulatory constraints before any model work begins
- Design and instrument feature pipelines; register reusable features in a feature store to prevent training-serving skew
- Build labeling workflows with inter-annotator agreement metrics (Cohen's kappa) and adjudication protocols
- Implement data validation schemas (e.g. Great Expectations) that gate pipeline runs on statistical sanity checks
- Create stratified train/validation/test splits that respect temporal ordering and avoid leakage
- Document dataset cards: source, collection period, known biases, and intended use
- Stand up experiment tracking (W&B or MLflow) before writing model code; every run logs hyperparameters, metrics, and artifacts
- For LLM tasks, build the eval harness first: automated test sets covering capability, refusal, and edge-case behaviour
- Iterate over retrieval strategies for RAG — chunking window size, overlap, embedding model choice, reranking — measuring MRR/NDCG on held-out queries
- For fine-tuning, compare full fine-tune vs. LoRA/QLoRA/PEFT adapters on compute cost and accuracy trade-offs
- Gate progression with a model card that records intended use, performance breakdown by slice, and known failure modes
- Package models with explicit dependency pinning; register artifacts in a model registry with semantic versioning
- Choose the right serving stack: FastAPI for low-QPS, Triton Inference Server for GPU batching, vLLM for LLM throughput, Modal or Baseten for serverless burst capacity
- Implement shadow deployment or canary rollout alongside the incumbent; define rollback criteria before go-live
- Expose standardised health, readiness, and metrics endpoints; integrate serving latency into the SLO dashboard
- For LLM systems, enforce guardrails (input and output filtering, PII detection) at the serving layer, not inside prompts alone
- Monitor for data drift (feature distribution shift) and concept drift (stable features, changed target relationship) using statistical tests and population stability index (PSI)
- Collect production prediction logs and — where feasible — delayed ground-truth labels for offline evaluation against the live distribution
- Instrument LLM systems for hallucination rate, guardrail trigger rate, latency percentiles, and downstream business metric impact
- Define retraining triggers (drift threshold crossed, accuracy below SLO, scheduled cadence) and automate the pipeline to execute them
- Run post-deployment A/B tests and multi-armed bandit experiments to validate that model updates move the business metric, not just the offline metric
Problem framing & data assessment
Define the decision the model must make, the business metric it must move, and the ground-truth label that represents success. Without alignment here, sophisticated models answer the wrong question at great expense.
Data engineering & labeling
Raw data almost never arrives in a form suitable for training. This stage builds the pipelines that transform, validate, and annotate data — and makes those pipelines reproducible so experiments are not contaminated by silent upstream changes.
Experimentation & evaluation
Rapid iteration over hypotheses — architecture, feature sets, prompt strategies, retrieval configurations — tracked so every experiment is reproducible and comparable. An eval harness is non-negotiable before the first model run, not an afterthought.
Deployment & serving
Getting a model into production is an infrastructure problem as much as an ML problem. Serving choices — batch vs. real-time, hardware, framework — must match the latency and throughput SLOs of the consuming application.
Production monitoring & iteration
Models degrade. Data drifts. Business context shifts. Production monitoring closes the loop so the team knows before users do — and a continuous retraining cadence keeps the model current.
Engineering Principles for Production AI
Opinions forged from shipping models that stayed in production. Each principle is a reaction to a common failure mode we have watched teams repeat.
An eval harness before a single prompt
The single most common LLM project failure is iterating on prompts without a structured test set. You cannot know whether a prompt change improved or regressed the model without automated evaluation. Build a representative set of input-output pairs covering the capability you need, failure modes you fear, and edge cases you discovered in requirements. Run it on every change. Intuition is not a test suite.
Most problems do not need a custom model
Fine-tuning a model is expensive to run, expensive to maintain, and expensive to debug when it misbehaves. Before committing to fine-tuning, exhaust prompt engineering and retrieval augmentation. RAG with a strong foundation model solves the majority of knowledge-grounding problems without a training run. Reserve LoRA/QLoRA fine-tuning for cases where latency constraints demand a smaller model, or where the required output style or domain vocabulary genuinely cannot be expressed in a prompt.
Ground LLMs in your data or expect hallucinations
Foundation models generate plausible text, not necessarily true text. For any task that requires factual accuracy over proprietary knowledge, implement retrieval-augmented generation: embed your corpus, store vectors in a vector database (Pinecone, Weaviate, or pgvector for existing Postgres stacks), retrieve the top-k chunks at query time, and pass them as grounded context. Evaluate hallucination rate explicitly — a faithfulness metric measuring whether the answer is entailed by the retrieved context is the minimum bar.
Monitor for drift, not just uptime
A model that is running is not the same as a model that is working. Infrastructure health checks tell you the container is alive; they tell you nothing about prediction quality. Instrument the feature distribution in production and compare it to the training distribution. When PSI or Jensen-Shannon divergence breaches a threshold, the model is operating out of sample. Alert on this as seriously as you alert on a 5xx spike.
Offline metrics lie — measure the business outcome
Improving AUC-ROC by 2 points on a validation set is not a business result. A recommendation engine that drives a 3% click-through improvement is. Offline metrics are useful proxies during development but should never be the final arbiter of a model release. Pair every model deployment with an A/B test whose primary metric is the business KPI — revenue, retention, task-completion rate — not the surrogate the model was optimised for.
Training-serving skew is silent and deadly
When the features computed at training time differ from the features computed at inference time — because of different code paths, preprocessing logic, or data sources — the model operates on inputs it was never trained on. This is training-serving skew, and it typically manifests as inexplicably poor production accuracy with perfect offline metrics. The fix is a feature store that serves the same computation to both the training pipeline and the prediction endpoint, enforced by contract.
Guardrails are infrastructure, not an afterthought
Input and output filtering, PII redaction, topic restriction, and toxicity detection must be built into the serving layer from day one. Prompts that contain safety instructions can be bypassed by adversarial inputs. Treat guardrails as a separate, independently deployable component — version them, test them against a red-team dataset, and monitor their trigger rate in production as a first-class signal.
Version data as seriously as you version code
A model is a function of its training data. If you cannot reproduce the exact dataset a model was trained on, you cannot reproduce the model. Dataset versioning — via DVC, Delta Lake snapshots, or a labeling platform export registry — is not optional overhead; it is the foundation of reproducible ML. When a model degrades in production, the first question is always 'what changed?' and the answer is almost always in the data.
ML & AI Engineering Stack
Tool choices driven by production requirements: reproducibility, scalability, and operational maintainability. Every tool here has replaced something that failed us in a real system.
Data & labeling
- DVCGit-native data versioning that tracks large dataset artifacts without storing them in the repository. Links each model artifact back to the exact dataset version and pipeline stage that produced it, making reproduction of any historical training run deterministic.
- Label StudioOpen-source annotation platform that supports text, image, audio, and multi-modal tasks in a single UI. Self-hostable, which matters when labeling data that cannot leave the customer environment. Exports in standard formats (COCO, YOLO, spaCy) that plug directly into training pipelines.
- Great ExpectationsData validation framework that expresses data quality rules as code and generates human-readable documentation. Gates pipeline runs on statistical assertions — null rate below threshold, column distribution within expected range — catching upstream data breakage before it silently corrupts a training run.
- FeastOpen-source feature store that serves pre-computed features to both offline training jobs and online inference endpoints from a shared definition. Eliminates training-serving skew by guaranteeing both consumers call identical transformation logic.
Experiment tracking & training
- PyTorchDe-facto standard for deep learning research and production. Dynamic computation graph makes debugging tractable; the ecosystem of pre-trained models (Hugging Face) and PEFT libraries (LoRA, QLoRA) is unmatched. Chosen over TensorFlow for its Python-native debugging experience and broader community support for modern architectures.
- scikit-learnThe right tool for classical ML: gradient-boosted trees, logistic regression, clustering, and preprocessing pipelines. Its Pipeline API enforces consistent preprocessing between training and serving, and its estimator interface makes it trivial to swap algorithms during experimentation. For tabular data it frequently outperforms neural approaches at a fraction of the computational cost.
- Weights & Biases (W&B)Experiment tracking with automatic hyperparameter logging, gradient histograms, and dataset artifact versioning. The sweep functionality runs distributed hyperparameter searches without custom orchestration code. Preferred over MLflow for collaborative teams because the hosted UI requires zero infrastructure to operate.
- MLflowSelf-hosted experiment tracking and model registry for environments where data cannot leave a VPC. The model registry provides the approval workflow (staging → production promotion) that separates an informal experiment from a governed production release. Integrates with Spark for large-scale feature engineering pipelines.
LLM & RAG
- LangChain / LlamaIndexOrchestration frameworks that abstract the retrieval-augmented generation pipeline — document loading, chunking, embedding, retrieval, reranking, and generation — into composable primitives. LlamaIndex has a stronger indexing abstraction for complex corpora; LangChain has broader ecosystem integrations. We use whichever fits the retrieval complexity of the project rather than picking a favourite.
- pgvectorVector similarity search as a Postgres extension, which means teams that already operate Postgres get approximate nearest-neighbour search without introducing a new infrastructure dependency. For corpora under ~5 million vectors with standard HNSW index configurations, query latency is competitive with dedicated vector databases. Dramatically reduces operational surface area for early-stage products.
- Pinecone / WeaviateManaged and self-hosted vector databases respectively, chosen when corpus scale, query throughput, or metadata-filtered similarity search exceeds what pgvector can handle ergonomically. Pinecone offloads all infrastructure; Weaviate offers a GraphQL API and hybrid sparse-dense retrieval (BM25 + vector) that improves recall for keyword-heavy queries.
- Sentence TransformersHugging Face library for producing dense passage embeddings from text. Supports asymmetric retrieval models (bi-encoder for recall, cross-encoder for reranking) which is the standard architecture for high-quality RAG pipelines. Self-hosted embedding avoids per-token API costs at scale and eliminates embedding API latency from the retrieval path.
Serving & inference
- FastAPIASGI framework for building model inference APIs when the serving pattern is request-response at modest QPS. Automatic OpenAPI schema generation means the model API is self-documenting from day one. Async support allows I/O-bound preprocessing (database lookups, feature store reads) to overlap with model inference.
- NVIDIA Triton Inference ServerGPU-optimised model serving with dynamic batching, concurrent model execution, and support for TensorRT, ONNX, and PyTorch backends. Dynamic batching coalesces multiple sub-millisecond requests into a single GPU kernel launch, dramatically improving GPU utilisation for high-throughput inference workloads.
- vLLMHigh-throughput LLM inference engine using PagedAttention to manage KV-cache memory with near-zero waste. Achieves 10-24x higher throughput than naive HuggingFace generate() at equivalent latency, which translates directly to lower cost-per-token at production request volumes. The standard choice for self-hosted open-weight model serving.
- Modal / BasetenServerless GPU platforms for burst inference capacity without reserved instance commitment. Modal's Python-native deployment model makes it trivial to ship a self-contained inference function with its dependencies and GPU requirements as a single decorated function. Used when traffic is spiky and the cost of idle GPU capacity exceeds the platform overhead.
Evaluation & guardrails
- RAGASReference-free evaluation framework for RAG pipelines that measures faithfulness (is the answer entailed by the retrieved context?), answer relevance, and context recall without requiring human-labelled ground truth for every query. Used to benchmark chunking strategies and embedding models before committing to a retrieval architecture.
- DeepEvalUnit-test-style LLM evaluation library that integrates with pytest, making eval runs a first-class part of the CI pipeline. Supports G-Eval (LLM-as-judge with explicit rubrics), hallucination metrics, and custom assertion types. The CI integration means a prompt regression is caught in pull-request review, not in production.
- Guardrails AI / NeMo GuardrailsStructured output validation and policy enforcement for LLM responses. Guardrails AI enforces Pydantic schemas on LLM output, eliminating the class of bugs where a malformed JSON response crashes a downstream parser. NeMo Guardrails uses a declarative Colang policy language for conversation-level safety rails, separating the safety policy from the application prompt.
- GarakOpen-source LLM red-teaming framework that systematically probes models for prompt injection, jailbreak susceptibility, data exfiltration, and toxic output. Used to produce a vulnerability report before any customer-facing LLM deployment. Running it is not optional for systems that accept untrusted user input.
Monitoring & observability
- Evidently AIOpen-source library for data drift and model performance monitoring. Generates interactive HTML reports and JSON metrics comparing production feature distributions to a reference baseline using statistical tests (Kolmogorov-Smirnov, chi-squared, PSI). Can run as a batch job feeding dashboards or as a real-time monitor in the serving path.
- Arize AI / WhyLabsManaged ML observability platforms that ingest production prediction logs and surface drift, data quality anomalies, and performance degradation without requiring teams to build and maintain monitoring infrastructure. Arize has stronger LLM tracing capabilities; both integrate with the major serving frameworks via lightweight SDK wrappers.
- OpenTelemetry + PrometheusFor teams that want monitoring inside their existing observability stack rather than a separate ML platform. Custom counters and histograms on serving latency, token usage, guardrail trigger rate, and model version distribution give engineering teams the same alerting and dashboarding experience they already have for their API infrastructure.
How we'd choose
There's rarely one right answer — these are the trade-offs we weigh before recommending an approach.
RAG vs Fine-tuning vs Prompt engineering
The majority of LLM product failures stem from applying fine-tuning where prompt engineering or retrieval would have been sufficient. Use this guide as a first-pass filter before committing engineering weeks to a training run.
| Criterion | Prompt engineering | RAG | Fine-tuning (LoRA/QLoRA) |
|---|---|---|---|
| When to use | Task fits in context window; behaviour is about instruction-following, not knowledge | Answers require accurate retrieval from a large, updateable proprietary corpus | Output style, format, or domain vocabulary cannot be expressed in a prompt; latency demands a smaller model |
| Data requirement | None — only prompt examples | Indexed corpus of documents; quality of chunking and embeddings is critical | Hundreds to thousands of high-quality input-output examples; bad data produces a confidently wrong model |
| Hallucination risk | High — model relies entirely on parametric knowledge | Low when grounding is enforced; faithfulness metrics must be measured | Moderate — model learns distribution of training data; may hallucinate outside that distribution |
| Knowledge update cost | Free — change the prompt | Low — re-embed and re-index new documents; no retraining | High — requires a new training run for every knowledge update |
| Latency profile | Single LLM call; latency dominated by model size | Adds retrieval round-trip (embedding + ANN query + reranking), typically 50-200ms overhead | Smaller fine-tuned models can be 3-10x faster than a larger base model doing the same task |
| Infrastructure overhead | Minimal — just the LLM API | Requires vector database, embedding pipeline, and retrieval service | Requires GPU training infra, experiment tracking, model registry, and separate serving endpoint |
| Recommended first attempt | Yes — always start here; most problems are solved at this layer | Yes — if prompt engineering hits a knowledge accuracy ceiling | No — only after RAG + prompt engineering are demonstrably insufficient |
Closed API model vs Open-weight self-hosted
Model hosting is an infrastructure and compliance decision as much as a performance one. The default answer is closed API; the exceptions are real but narrow.
| Criterion | Closed API (GPT-4o, Claude, Gemini) | Open-weight self-hosted (Llama, Mistral, Qwen) |
|---|---|---|
| Data privacy | Data leaves your infrastructure; BAA required for regulated data; some providers offer private deployments | Data never leaves your VPC; mandatory choice for healthcare, legal, or government workloads under strict data residency requirements |
| Quality ceiling | State of the art on most benchmarks; updated by the vendor without action required | Rapidly narrowing gap on many tasks; 70B-parameter instruction-tuned models match closed APIs on focused domains when fine-tuned |
| Operational burden | Zero infrastructure — API key and HTTP client | Significant: GPU procurement or cloud instance management, model serving (vLLM), version pinning, scaling, and 24x7 on-call |
| Cost at scale | Per-token cost scales linearly with volume; can become expensive above ~100M tokens/day for complex tasks | Amortised GPU cost favours self-hosting above ~50-100M tokens/day depending on model size and hardware; break-even is workload-dependent |
| Latency control | Subject to API provider SLOs and rate limits; P99 latency is not under your control | Full control over hardware, batching strategy, and quantisation level; predictable latency under your own SLO |
| Fine-tuning access | Limited — vendor-provided fine-tuning endpoints with restricted architecture access; no LoRA/PEFT control | Complete — full access to weights enables LoRA, QLoRA, DPO, and RLHF; can publish and version adapters independently |
| Recommended default | Yes — default choice for new products; validate product-market fit before investing in self-hosted infrastructure | No — consider after data residency, cost, or latency requirements cannot be met by closed API providers |
MLOps Maturity Model
MLOps maturity describes how much of the ML lifecycle is automated, governed, and continuously improving. Most teams land at Level 1 and mistake notebooks for production. The target is Level 2: automated, monitored, and recoverable.
Level 0 — Notebooks & manual releases
- Model training happens in Jupyter notebooks on individual engineers' machines
- No experiment tracking — results are recorded in spreadsheets or not at all
- Deployment is a manual copy of a pickle file to a server; rollback requires re-running the notebook
- No monitoring beyond infrastructure health checks; model degradation is reported by users, not detected by the team
- Data preprocessing is duplicated between the notebook and the serving code, creating training-serving skew
Level 1 — Automated training & reproducible deployment
- Training pipelines are parameterised scripts executed in CI/CD; every run logs to W&B or MLflow with full hyperparameter and artifact records
- Models are registered in a model registry with semantic versioning and a staging/production promotion workflow
- Deployment is automated via a CD pipeline; canary rollout with defined rollback criteria replaces manual deployments
- A feature store or shared preprocessing library eliminates training-serving skew
- Basic data drift monitoring runs on a schedule and alerts when distribution shift exceeds a configured threshold
Level 2 — Continuous, monitored & self-healing
- Retraining triggers automatically when drift thresholds are crossed or offline evaluation below SLO is detected; no human intervention required for routine retraining
- Online and offline metrics are jointly monitored; business KPIs are the primary alert signal, not just model accuracy
- Shadow models run continuously against live traffic; promotion decisions are data-driven from A/B test results
- Dataset versioning and lineage tracking provide a full audit trail from production prediction back to the training example that shaped it
- Red-team and eval suite runs are gated in CI; a regression in any evaluation dimension blocks the deployment pipeline
Anti-Patterns We Fix
These are the four failure modes we encounter most often when inheriting or auditing ML systems. Each one is avoidable with the right structure early.
The model that never ships
The team spends months improving offline metrics — AUC from 0.82 to 0.86, F1 from 0.74 to 0.79 — but the model never reaches a production endpoint. The lab environment has different data access, different preprocessing, and different infrastructure than production, making each release attempt a crisis. Eighty-five percent of the engineering effort is sunk into the 15% of the problem that happens before deployment.
Ship a baseline model to production in week one, even if it barely beats a rule-based heuristic. The act of deploying forces the team to confront serving infrastructure, API contracts, monitoring, and rollback mechanics early. Subsequent iterations improve an already-live system rather than fighting a big-bang deployment at the end of the project.
No evaluation harness
Prompt changes, model version upgrades, and retrieval configuration updates are evaluated by manually reading five example outputs. There is no structured test set, no automated scoring, and no historical record of what each configuration produced. The team cannot tell whether a change improved or regressed quality across the distribution — only whether it felt better on the examples they happened to check.
Build an eval harness before writing the first prompt. A representative test set of 50-200 input-output pairs covering capability, known failure modes, and edge cases is sufficient to start. Automate scoring using a combination of deterministic assertions, embedding similarity, and LLM-as-judge with an explicit rubric. Run the harness on every pull request via CI. The harness is the product as much as the model is.
Training-serving skew
The model achieves 91% accuracy on the validation set but 73% in production. Investigation reveals that categorical features are one-hot encoded differently in the training pipeline and the inference service, timestamp features use UTC in training but local time in production, and a data imputation step was added to the notebook but never ported to the API. The model is operating on inputs it was never trained on, and there is no alerting to surface this.
Centralise all feature computation in a shared library or feature store that is imported by both the training pipeline and the serving endpoint. Write integration tests that generate a known input, run it through both paths, and assert that the resulting feature vectors are byte-for-byte identical. Add feature distribution monitoring to production that compares live inputs against the training baseline and alerts on divergence.
Prompt-and-pray (no grounding or guardrails)
An LLM is deployed with a system prompt and an input box, with no retrieval grounding, no output validation, and no safety filtering. The model confidently cites documents that do not exist, reveals information from other users' sessions through prompt injection, and occasionally produces outputs that violate the company's brand or compliance requirements. Issues are discovered by customers and escalated to the engineering team, which has no observability into what the model produced.
Implement RAG to ground the model in authoritative documents before launch; measure faithfulness with RAGAS to establish a hallucination baseline. Deploy input and output guardrails as a separate service layer — not in the system prompt — covering PII detection, topic restriction, and toxicity filtering. Run a structured red-team exercise using Garak before any customer-facing release. Log every prompt and completion with a correlation ID, and monitor guardrail trigger rate as a production signal.
Go deeper
AI & Machine Learningcovers a lot of ground. Here are the specific things we're most often asked to build.
Common questions
Do we need a huge dataset to use ML?
Not always. Some problems need scale; many are solved with a few thousand well-labelled rows, a smart baseline, or an LLM with the right context window. We will tell you honestly which case you are in before any work starts.
How do we know the model actually works?
Every engagement includes evaluation against held-out data and the specific failure modes that matter to your use case. We would rather show you the limits up front than oversell accuracy you will not see in production.
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