Questions, answered directly

Mifa delivers enterprise AI solutions, agentic AI systems, generative AI and machine learning, intelligent automation and RPA, custom software development, cloud and DevOps, data engineering, business intelligence, ERP/CRM, and cybersecurity. Engagements range from a single AI use case to multi-year platform programs, always delivered by dedicated senior teams rather than rotating bench staff.

Most engagements start with a free 45-minute consultation followed by a 2–4 week discovery or pilot phase. Discovery produces an architecture proposal, a delivery roadmap, and a fixed budget range for the first release — so you commit to a full build only after seeing concrete plans and, for AI projects, measured results on your own data.

We offer three models: team augmentation (senior engineers embedded in your teams, monthly), project delivery (fixed-scope builds with milestone pricing), and enterprise partnership (a dedicated multi-disciplinary squad with a quarterly roadmap). Many clients start with a project and evolve into a partnership once the first system ships.

Mifa has 400+ engineers and researchers based at our Hyderabad headquarters, serving clients in 12 countries. Delivery teams work in your time zone with at least 4 hours of daily overlap, and on-site presence is available for discovery, launches, and quarterly reviews.

Both. Roughly 60% of our clients are Fortune 1000 enterprises and 40% are mid-market companies (500–5,000 employees). Mid-market engagements typically start with a focused project in the $150K–$500K range; enterprise programs scale from there. The engineering standards, security posture, and delivery process are identical at both tiers.

Yes — roughly a third of our engagements begin with a codebase or AI system we didn't build. We start with a 2–3 week technical audit covering architecture, code quality, security, and operational maturity, then propose either incremental modernization (the usual answer) or a re-platform with a strangler-fig migration path. You receive the audit findings regardless of whether you proceed with us.

We are model-agnostic: we build on the leading frontier model APIs, open-weight models (Llama, Mistral, Qwen families) served on your infrastructure, and domain-specific fine-tunes. Platform-wise we work across AWS, Azure, and GCP, with vector search, orchestration, and MLOps tooling chosen per requirement — the model is a component in the architecture, not the architecture.

Agentic AI systems don't just answer questions — they plan and execute multi-step work: querying systems, calling APIs, making within-policy decisions, and escalating exceptions to humans. A chatbot ends at a reply; an agent ends at a completed task, such as a resolved invoice exception or a processed claim. Our agent deployments include tool permissioning, budget enforcement, human approval checkpoints, and full audit trails as standard.

Through layered controls rather than trust in the model: retrieval-grounded generation with citation requirements, schema-validated outputs, confidence thresholds that route uncertain cases to humans, and evaluation suites that measure groundedness before every release. For regulated use cases we add mandatory human review on irreversible actions. No production system we ship relies on 'the model is usually right' as a control.

No. Client data is never used to train models for other clients, and we configure all third-party model APIs with training opt-outs and zero-retention options where available. When fine-tuning on your data, the resulting model weights belong to you and are deployed in your environment or an isolated tenant.

No — but you should budget for data work inside the project, because it's typically 30–50% of the effort. We assess data readiness during discovery, build the minimum data engineering needed for the first use case (not a multi-year data program), and establish data contracts so quality holds after launch. Waiting for a perfect data foundation before starting AI is the most expensive mistake we see.

Yes. We regularly deploy open-weight models, vector stores, and orchestration entirely within client-controlled infrastructure — on-premises GPU clusters, sovereign cloud regions, or air-gapped environments for government work. The tradeoff is model capability versus control; we benchmark both options against your quality bar during discovery so the decision is empirical.

AI pilot projects typically run $75K–$200K over 6–10 weeks; production AI systems and custom platforms range from $250K to $1.5M+ depending on scope; team augmentation starts around $18K per engineer-month. Every engagement gets a fixed budget range after discovery — we don't quote precise figures before understanding your systems, and we recommend distrust of anyone who does.

A scoped first use case typically reaches production in 12–20 weeks: 2–4 weeks discovery, 6–10 weeks build with weekly demos, and 2–4 weeks of hardening, evaluation, and rollout. Pilots on real data land in 6–8 weeks. Timelines extend mainly with integration surface area and compliance review cycles — not model work, which is rarely the bottleneck.

Five phases: Discover (workshops, data and systems audit), Design (architecture, security review, delivery plan), Build (2-week sprints, demo every sprint, CI/CD from week one), Validate (evaluation suites, UAT, performance and security testing), and Operate (monitored rollout, runbooks, handover or managed operation). You see working software every two weeks, and budgets are re-confirmed at each phase gate.

You do. All code, models fine-tuned on your data, documentation, and infrastructure definitions are your property under our standard MSA, assigned on payment. We retain only our pre-existing internal tooling and accelerators, which are licensed to you perpetually at no cost where used in your solution.

Every project defines 2–4 business metrics before build starts — cost per processed document, handling time, conversion rate, forecast error — with a measured baseline. We instrument these into dashboards from day one and report against them monthly. Model metrics (accuracy, groundedness, latency) are tracked as engineering health, but the business metric is the definition of done.

Yes, at three levels: warranty (defect fixes for 90 days, included), managed operations (SLA-backed monitoring, model evaluation, and continuous improvement, from $8K/month), and embedded evolution (a retained squad shipping your roadmap). We also fully hand over to internal teams when preferred — with documentation, runbooks, and paired transition sprints.

Mifa maintains SOC 2 Type II attestation and ISO 27001 certification, with annual third-party penetration testing of our delivery infrastructure. Client work follows a documented secure development lifecycle: threat modeling at design, dependency and secret scanning in CI, and least-privilege access with full audit logging on all client environments.

Yes. We deliver HIPAA-compliant systems with BAAs in healthcare, GDPR-compliant architectures with EU data residency for European clients, and systems aligned to SOX, PCI DSS, and model-risk-management guidance (SR 11-7 style validation) in financial services. Compliance requirements are captured during discovery and built into the architecture, not audited in afterward.

Client data stays in client-controlled environments wherever possible — our engineers work against your cloud tenancy with your IAM, not copies on our laptops. Where data must transit our systems, it's encrypted in transit and at rest, access is role-scoped and logged, and data handling terms (residency, retention, deletion on exit) are contractually defined in the MSA and DPA.

We treat LLM applications as untrusted-input systems: retrieved content and user input are never granted tool authority by themselves, write-capable actions are gated by allowlisted, schema-validated tool calls with structural permission checks, and agent actions run under budgets and approval thresholds. We test against prompt-injection suites before release and monitor for anomalous tool-call patterns in production.

Yes — we build compliance into the system design. For each use case we classify risk under the EU AI Act, implement the corresponding obligations (documentation, human oversight, logging, accuracy monitoring), and deliver the technical documentation your compliance team needs. For high-risk categories we implement full decision traceability so every automated outcome can be explained and audited.

Absolutely — and enterprise clients usually do. We support vendor security assessments with our SOC 2 report, ISO certificates, penetration-test summaries, and completed standard questionnaires (SIG, CAIQ), and we'll join calls with your security team. Typical procurement-to-signature security review takes two to four weeks; we've never lost a deal to one.